Horticulture aided by autonomous systems

ABSTRACT

Techniques and examples for servicing a horticultural operation are described. A method may involve receiving data from one or more autonomous vehicles. The data pertains to the horticultural operation or one or more targets located within the horticultural operation. The received data is analyzed to determine one or more conditions of the horticultural operation or one or more targets. Based on the analyzing, one or more recommendations for addressing the one or more conditions are determined. The determined conditions and recommendations are sent to a user interface. When authorized via the user interface, data is transmitted to the one or more autonomous vehicles that are indicative of follow-on actions for the horticultural operation or target. Additional data is received, when available, based on the follow-on actions for further analysis.

BACKGROUND

Growers and farmers in the horticultural industry strive to enhance cropyield to maximize production and revenue. In order to achieve theseobjectives, horticultural tasks or actions need to increase efficienciesin planting, cultivating, and harvesting of plants. In general, theseprocesses rely on a master grower, typically an experienced farmer,gardener, or agronomist, who oversees the horticultural tasks oractions. The master grower is usually required to physically go out to ahorticultural field and spot-check sample plants in selected areas ofthe field. Upon examining a sample plant, the master grower may identifya horticultural status of the sample plant, such as a horticulturalissue the sample plant may be having. Depending on the findings, themaster grower may subsequently decide to check more plants in theneighborhood of the sample plant to determine whether the issue is aproblem isolated to the sample plant or a general problem in theneighborhood. The master grower may also take measurements ofenvironmental variables in the neighborhood to aid in determining apossible cause of the issue.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures, in which the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Somearticles in the figures may be referenced by an alphanumeric labelstarting with a letter rather than a number. Such an alphanumeric labelmay first appear in any figure. The use of the same reference numbers orthe same alphanumeric labels in different figures indicates similar oridentical items.

FIG. 1 illustrates a context diagram of an Autonomous HorticulturalFeedback (AHF) system capable of performing an AHF process for ahorticultural operation.

FIG. 2 illustrates a positioning mechanism applicable to an AHF system.

FIG. 3 demonstrates an example operation dashboard of an AHF system.

FIG. 4 illustrates a local area map of a horticultural field.

FIG. 5 illustrates a physical structure map of a horticultural field.

FIG. 6 illustrates a grow operation map of a horticultural field.

FIG. 7 illustrates a field activity map of a horticultural field.

FIG. 8 illustrates a restricted-zone (RZ) map of a horticultural field.

FIG. 9 illustrates example paths for autonomous vehicles utilized by anAHF system.

FIG. 10 demonstrates an example dashboard of a horticultural field.

FIG. 11 demonstrates an example mission dashboard of a horticulturalfield.

FIG. 12 illustrates plant unit lists of a horticultural field as well asplant units in a corresponding local area of the horticultural field.

FIG. 13 is a block diagram showing various components of a computingserver of an AHF system.

FIG. 14 illustrates a flow diagram of an example process for executing ahorticultural mission of an AHF process.

FIG. 15 illustrates a flow diagram of an example process for updating apath.

FIG. 16 is a block diagram showing various components of an exampleautonomous vehicle.

DETAILED DESCRIPTION

In order for growers and farmers in the horticultural industry toenhance crop yield and maximize production and revenue, horticulturaltasks or actions often involve more than planting, cultivating, andharvesting of plants. For example, horticultural tasks or actions caninclude a horticultural feedback process. The horticultural feedbackprocess includes the constant monitoring and examining of plants duringvarious growing phases of the plants. Based on results of the monitoringor examination, early identification of any horticultural issues may bepossible, and remedial solutions to address the issues may be determinedand applied accordingly. The effectiveness of the remedial solutions maybe assessed by further monitoring and examination of the plants afterthe remedial solutions are applied, thereby completing the horticulturalfeedback process.

In general, this process relies on a master grower, typically anexperienced farmer, gardener, or agronomist, to perform thehorticultural feedback process. The master grower is typically requiredto physically go out to a horticultural field and spot-check sampleplants in selected areas of the field. Upon examining a sample plant,the master grower may identify a horticultural status of the sampleplant, such as a horticultural issue the sample plant may be having.Depending on the findings, the master grower may subsequently decide tocheck more plants in the neighborhood of the sample plant to determinewhether the issue is a problem isolated to the sample plant or a generalproblem in the neighborhood. The master grower may also takemeasurements of environmental variables in the neighborhood to aid indetermining a possible cause of the issue. Accordingly, the mastergrower may determine one or more remedial solutions to be applied to theneighborhood to address the horticultural issue. However, the ability ofthe master grower or farmer to perform the described tasks is notscalable, and the master grower or farmer will be overwhelmed by theamount of tasks as the size and yield of the operation grows. This canbe a limiting factor not only in the growth of operations, but inimproving the efficiencies of existing operations. The horticulturalfeedback process can be heavily labor-intensive and time-consuming for ahuman master grower. The problem can be exacerbated as the physicalscope of modern industrial horticultural operations become larger, oftenin tens or hundreds of acres. Moreover, horticultural operations withinone industrial horticultural business entity may possibly encompassseveral horticultural fields or greenhouses that are at differentgeographic locations and/or exposed to different climates or growingenvironments. Even if a sufficient number of master growers can beresourced for performing the horticultural feedback process,inconsistency between individual master growers may compromise theeffectiveness of the horticultural feedback process.

A horticultural feedback process can be used to increase yield and/orproduce quality of a grow operation. Through monitoring plants of thegrow operation as the plants gothrough various growing stages,horticultural problems of the grow operation may be identified, andsubsequent remedial course of action may be determined and taken toaddress the horticultural problems.

A horticultural feedback process may include regular and periodicmonitoring of a grow operation to collect horticultural data about thegrow operation, which traditionally requires manual spot-checks onplants. The process is typically labor-intensive and time-consuming whenexecuted by humans, especially if the grow operation is implementedacross a large horticultural field or at multiple geographic locations.Aspects of the present disclosure address this problem. Further detailsare described below.

The present disclosure is directed to techniques for automating thehorticultural feedback process so that human labor involvement in theprocess may be reduced or minimized and the process as a whole may bemore efficient and/or scalable. Various embodiments described herein aregenerally directed to methods and systems for automatically executinghorticultural tasks using autonomous devices and analysis systems thatautomate the analysis, diagnosis, and feedback tasks. In one example,various autonomous devices, such as robots, unmanned aerial vehicles(UAVs), together with various sensors and other data capture devices maybe utilized to aid the horticultural data collection of the process.

The disclosed system automates the discovery process of potential issuesin the field. For example, one or more autonomous devices canperiodically (e.g., at least once a day) traverse and analyze theentirety of a growing operation, identify issues, and inform the mastergrower of the issues. This enables growers to scale up their operationswith no upper-bound on size.

In one embodiment, methods and systems disclosed herein canautomatically perform a variety of horticultural missions which aim toperform actions with respect to one or more targets (e.g., target plant930 of FIG. 9) of a horticultural field. Target plants may bedistinguished from non-target plants using sensors and other devices. Ahorticultural field may be divided into many local areas, and each localarea may grow a certain kind of plant of some quantity. Based on anidentification of the target, it can be determined which one of the manylocal areas the target is located within. The horticultural mission maybe automatically generated based on one or more objectives that aresupervised by the master grower or farmer.

In one embodiment, a particular horticultural mission may be performedby an autonomous vehicle (e.g., vehicle 920). A path (e.g., path 910)may be automatically determined so that the autonomous vehicle maytravel along the path and arrive at the local area where the target islocated. One or more restricted zones (e.g., restricted zones 941-946)within the horticultural field may be identified, and the path may berevised to avoid any restricted zones within the horticultural field. Inaddition to restricted zones and macro-objects that can be determinedthrough the observation of, for example, a wide-area field-of-view (FOV)camera or other device, the autonomous vehicles may be configured todetect and avoid smaller objects that may interfere with the movement ofthe vehicle. The vehicle may have an on-board real-time vision systemthat is capable of dynamic path planning around smaller objects, such asposts, wires, or humans that that were not previously detected.Additionally, such real-time vision systems can be used to aid thenavigation of the vehicle and augment on-board guidance systems such asinertial navigation systems. After arriving at the local area, theautonomous vehicle may locate the target within the local area based onthe identification of the target, and then subsequently perform theaction with respect to the target.

The automated horticultural feedback process using such devices may bereferred as an Autonomous Horticultural Feedback (AHF) process. An AHFsystem comprising various hardware and software components, as describedin detail below, may be employed to perform an AHF process. It should benoted while some of the examples described herein are illustrated in thecontext of ground robots, the described principles can be implementedwith any type of autonomous moving or self-moving vehicle or device. Theterm “autonomous device” or “autonomous vehicle” may include any suchvehicle or device and is used interchangeably herein. It should also benoted while some of the examples described herein are illustrated in thecontext of sensors and image capture devices, the described principlescan be implemented with any type of data capture device, including RFsensors, audio sensors, particle capture and analysis devices (e.g.,soil capture), and the like. The term “sensor” or “data capture device”may include any such device and is used interchangeably herein.

Additionally, the control of the autonomous vehicles may be centralizedusing one or more control systems that may be implemented on-premises orremotely, such as in the cloud. Additionally, the autonomous vehiclesmay form a decentralized mesh network that propagates data along eachnode. In this way, range of the AHF system may be indefinitely extended.

It should also be noted that various types of vehicles may be used toaugment the techniques described herein. For example, a camera moduleand/or sensor payload, or any of the sensors described herein, can beattached to an agricultural vehicle to transmit data to the AHF system.Such vehicles can include but is not limited to seed drills,cultipackers, movers, farm trucks, tractors, plows, and manurespreaders. Any of these vehicles can be manned (manual) or autonomous.Additionally, a camera module and/or sensor payload can be attached to anon-agricultural vehicle, such as an all-terrain vehicle (ATV),automotive vehicle, non-motorized cart, and the like, to transmit databak to the AHF system.

In some embodiments, sensors may be coupled to non-vehicles to augmentthe techniques described herein. For example, a camera module and/orsensor payload can be attached to a backpack, jacket, handheld module,hat, or any other apparatus that enables an individual to carry apayload to transmit data back to AHF system. All of the describedmethods can be used to supplement and enhance the data collectioncapabilities of the AHF system.

Horticultural data collected by an AHF system in an AHF process mayinclude plant-related information as well as non-plant-relatedinformation. The plant-related information may include, but is notlimited to, height of a plant, color of leaves, density of buds orflowers, size of fruits or grains, etc. The AHF system may facilitatecollecting plant-related information via one or more autonomous devicesinstead of a human grower. For example, a ground robot may be equippedwith one or more still image cameras or video cameras. The AHF systemmay maneuver the ground robot to a target plant within the horticulturalfield to capture pictures (i.e., still images) or a video of the targetplant. The pictures or the video may be stored in a digital format, andsubsequently analyzed by image processing algorithms to extract variousplant-related information including the ones mentioned above. Ifequipped with multiple cameras, the ground robot may capture a binocularor multi-ocular image or video, which can indicate size of objects inthe image or video.

The non-plant horticultural data collected by the AHF system may includecontextual information that is not directly measured or gathered from aplant but is otherwise related to the growing environment of the plant.For example, the contextual information may include a temperaturereading, a humidity reading, or an illumination reading of the ambientenvironment of a plant. The contextual information may also include anair pressure reading, or a pH level reading of the soil or water inwhich the plant is planted or immersed. The contextual information mayalso include data collected from CO2 sensors, and may include vapor(VPD). The contextual information may further include weatherinformation, such as cloud cover, seasonality, or precipitation. Suchdata can be obtained from third party sources, or from weather stationsat the growing operation itself. Similar to the collection of theplant-related information, the AHF system may also facilitate collectingcontextual information via one or more robots instead of a human grower.

In some embodiments, an autonomous device such as a ground robot may beequipped with various sensors such as a thermometer, a hydrometer, alight meter, a barometer, an anemometer, and/or a pH meter. The AHFsystem may cause the ground robot to approach a target plant within thehorticultural field, and collect contextual information such as ambienttemperature, humidity, illumination, air pressure, wind speed, or pHlevel using the sensors equipped on the ground robot. In someembodiments, a video camera or a still image camera may be equipped on aground robot, and the camera may be used to take video or pictures ofplants or other non-plant objects in the horticultural field. Softwarealgorithms, procedures, or programs may analyze the video or thepictures to extract contextual information around a target plant, suchas an illumination condition, a weather condition, other horticulturalactivities being conducted, unexpected situations in thefield/greenhouse, etc.

Alternatively, a ground robot may collect contextual information withoutbeing equipped with various sensors or cameras. That is, the varioussensors described above may, instead of being disposed on a groundrobot, be deployed in the horticultural field. The AHF system maymaneuver a ground robot to a target plant, and the ground robot maycommunicate with the sensors deployed in a vicinity of the target plantto receive contextual information reported by the sensors. Some examplemethods for wireless communication to sensors include Bluetooth, NFC,LoRA, and RFID.

A further part of the AHF process may include one or more analysisfunctions that are configured to analyze the collected data and identifyproblems and issues based on the horticultural data that has beencollected as described above. The process of obtaining horticulturalknowledge and data over time from many multiple sources in the mannerdescribed can provide analysis and insights that may be difficult for asingle farmer to arrive upon alone. The analysis functions may furtherdetermine possible causes of the problems, as well as a remedial courseof action, such as making a diagnosis and determining a treatment planbased on various observed symptoms or conditions on. In someembodiments, the AHF system may still rely on an experienced humanmaster grower to review the results of the analysis and identificationof horticultural problems based on the horticultural data collected bythe robots.

In some embodiments, the AHF system may incorporate computer-basedmachine learning capabilities or artificial intelligence (AI) to aid inthe process of identifying a problem and a remedial solution based onthe horticultural data collected from the field. The AHF system usingthis approach may facilitate a faster, more consistent, and morescalable AHF process. Such an AHF system may be referred as anArtificial Intelligence and Automated Horticultural Feedback (AIAHF)system. Compared with reliance on a human master grower, an AIAHF systemmay facilitate faster experience accumulation and more efficientlearning, as the AIAHF system is able to cross-reference tohorticultural feedback processes from a large number of grow operations,possibly across a wide range of geographic locations, whereas a humanmaster grower is typically limited to a significantly fewer number ofgrow operations at one or a few locations.

In some embodiments, after an initial collection of horticultural data,the AHF system may decide to collect additional horticultural databefore identifying a problem and/or prescribing a remedial solution.Namely, ground robots may be sent in several “waves” for collectingdifferent kinds of horticultural data, or a same kind of horticulturaldata at different moments in time, before identifying a problem and/orprescribing a remedial solution.

The AHF system may also facilitate the execution or implementation of aremedial solution. For example, a remedial solution as determined may becommunicated to human workers in the field, and the human field workersmay operate certain horticultural tools, vehicles, or equipment, such astractors, soil mixers, pruners, etc., to apply the remedial solution totarget plants. In some embodiments, the AHF system may carry out aremedial solution using automatic or semi-automatic horticulturalequipment. For example, the remedial solution may be increasing thewater irrigation frequency from twice a day to four times a day. Theremedial solution may be transmitted to a computer-controlled sprinklersystem on the field and so applied to target plants. In someembodiments, the AHF system may carry out a remedial solution usingrobots in addition to human field workers. That is, robots may beutilized to perform a remedial solution. For example, the AHF system maydetermine that certain pesticide needs to be applied to a specific areaof the horticultural field, and the AHF system may direct one or moreground robots to carry the pesticide to the specific area and apply thepesticide to the plants in the specific area.

A follow-up step may conclude the horticultural feedback process,wherein further monitoring of the plant after a remedial solution isapplied may reveal whether the remedial solution has mitigated theproblem successfully. The AHF system may facilitate an automation ofthis follow-up step by sending ground robots to collect horticulturaldata of a target plant after a remedial solution has been applied to thetarget plant. In some embodiments, the AHF system may send a groundrobot to obtain a physical sample from a plant for further analysis.

Any part of the AHF process may be monitored by a master grower throughone or more interfaces configured to provide access to the AHF systemusing various types of devices including mobile devices to enablecontinuous and as-needed communications. For example, a master growermay use a phone or desktop-based application to consume data. In someembodiments, an application programming interface (API) may be providedto facilitate the servicing of input and output to the AHF system.

The techniques pertinent to an AHF system enable the modernhorticultural industry to manage grow operations in a scalable way,regardless of the size of the horticultural fields, the number ofgreenhouses, or whether the horticultural fields/greenhouses are at sameor different geographic locations. Specifically, with the describedtechniques, the horticultural feedback process is no longer limited bythe availability of experienced human master growers, a resource that isbecoming more and more scarce and costly. As a grow operation scales up,the described techniques would ensure predictable crop yield and/orreadiness with minimum increase in overhead cost.

The techniques described herein may be implemented in a number of ways.Example implementations are provided below with reference to thefollowing figures.

FIG. 1 provides an example context diagram illustrating an AHF system100 configured to perform AHF operations for a horticultural operation.A horticultural operation may include one or more outdoor open spacesthat receive natural light (e.g., sunlight). Alternatively oradditionally, the horticultural operation may include one or moreindoor, enclosed spaces that receive natural light through windowsand/or artificial light from a man-made light source (e.g., lamps). Thehorticultural operation shown in FIG. 1 includes an outdoorhorticultural field F01 and an indoor greenhouse G02. Within the scopeof present disclosure, the terms “horticultural field” and “greenhouse”may be used interchangeably when the context is irrelevant to theoutdoor/indoor nature of a particular embodiment. Horticultural fieldsand/or greenhouses of a horticultural operation may be at geographiclocations that are close to or away from each other. Each of thehorticultural fields and the indoor greenhouses may cultivate one ormore grow operations, such as grow operations 101 and 102 in field F01,as well as grow operation 103 in greenhouse G02. Each grow operation maygrow a specific kind of plant or crop starting on a specific date. It'snot necessary that all the growable space within a horticultural fieldor greenhouse be occupied by grow operations at any given time. Forexample, as shown in FIG. 1, field F01 still has some growable spacesthat are not currently being used for growing plants or crops.

To collect horticultural data of a grow operation, AHF system 100 mayemploy a plurality of field sensors, such as sensors 111 deployed infield F01 or sensors 112 deployed in greenhouse G02. Sensors 111 and 112may include, for example but not limited to, a thermometer for measuringan ambient temperature, a hydrometer for measuring an ambient humidity,a light meter for measuring an ambient illumination, a barometer formeasuring an ambient air pressure, an anemometer for measuring anambient wind speed, or a pH meter for measuring a pH level reading ofthe soil or water in which a plant is planted or immersed. Each ofsensors 111 and 112 may be communicatively coupled to a local serverphysically located within a vicinity of field F01 or greenhouse G02. Forexample, local server 121 may be located in or close to field F01, andsensors 111 may be connected with local server 121 via wired or wirelesscommunication links so that horticultural data of grow operations 101and 102 may be collected by sensors 111 and subsequently transmitted toand stored in local server 121. Similarly, local server 122 may belocated in or close to greenhouse G02, and sensors 112 may be connectedwith local server 122 via wired or wireless communication links so thathorticultural data of grow operation 103 may be collected by sensors 112and subsequently transmitted to and stored in local server 122. Thehorticultural data may be stored in local servers 121 and 122 with timestamps. That is, each entry of the horticultural data may denote whichfield sensor (i.e., which one sensor of sensors 111 or 112) the dataentry was measured by, as well as a moment in time (i.e., a time stamp)at which the specific data entry was recorded by the specific sensor.

In some embodiments, power needed to operate sensors 111 and 112 may besupplied from a power supply by dedicated power wires. In someembodiments, power needed to operate sensors 111 and 112 may be suppliedby solar panels, batteries, or other portable power sources disposed atrespective locations of sensors 111 and 112.

In some embodiments, sensors deployed in a horticultural field may nottransmit the horticultural data as collected to a local server via adirect communication link, be it a wired or wireless link. Rather,autonomous vehicles, such as ground robots, may be utilized to travel toa vicinity of a field sensor to collect horticultural data from thespecific field sensor. That is, AHF system 100 may command one or moreautonomous vehicles to travel to a plant of interest within thehorticultural field, and subsequently collect horticultural datarelevant to the plant of interest from one or more field sensors thatare deployed in a vicinity of the specific plant.

In some scenarios, this approach of collecting horticultural data sensedby field sensors using robots and other autonomous devices may bepreferred over direct communication links between field sensors and thelocal server. The scenarios may include a horticultural field that isrelatively large in area and thus the infrastructure deployment ofdirect communication links between field sensors and the local server,whether wired or wireless, is relatively expensive or at least noteconomical. In some embodiments, a combination of both approaches ofcollecting horticultural data is possible. For example, for sensors thatare physically located in a vicinity of a local server, directcommunication links may be installed or otherwise established to couplethose sensors to the local server. For sensors that are physicallylocated rather far away from the local server, ground robots or otherautonomous vehicles may be utilized to collect horticultural data asdescribed above.

In some embodiments, AHF system 100 may assign missions to ground robotsfor performing certain steps of an AHF process, such as collectinghorticultural data from field sensors. After being assigned a mission, aground robot may maneuver to the horticultural field to execute themission. In other embodiments, ground robots may perform missionswithout being directed by the AHF system 100 if conditions aresufficient to warrant a new mission.

When a ground robot is not executing a mission, the ground robot may bedocked in a vehicle bay. In an example embodiment, a vehicle bay may bea structure in or near the horticultural field, able to host a pluralityof ground robots therein. Each horticultural field may have one or morevehicle bays. As shown in FIG. 1, field F01 has two vehicle bays 141,and greenhouse G02 has one vehicle bay 142. Each of vehicle bays 141 and142 may host or otherwise accommodate one or more ground robots ofground robots 131 or ground robots 132 when the one or more groundrobots are not deployed.

In some embodiments, a vehicle bay may serve as a power station toground robots. Namely, a vehicle bay may be equipped to provide power orfuel to the ground robots, UAVs, or other devices docked therein. Groundrobots 131 and 132 and UAVs 133 of AHF system 100 may be powered byelectricity, fuel, or a hybrid of both electricity and fuel. Some of theground robots 131 and 132 and UAVs 133 may be equipped with a hybridengine to convert fuel to electricity. When docked in a vehicle bay ofvehicle bays 141 and 142, a ground robot of ground robots 131 or groundrobots 132 or UAVs 133 may have its fuel tank replenished, or batteryrecharged, via the vehicle bay. In some embodiments, a vehicle bay mayserve as a data transfer station for a ground robot docked therein.Namely, a vehicle bay may be equipped with a storage device, and variousdata may be transferred from the ground robot to the storage device, orvice versa, when the ground robot is docked inside the vehicle bay.Moreover, a vehicle bay may exchange data with a ground robot via awireless means, especially when the ground robot or UAV is within awireless communication range from the vehicle bay. The storage devicemay be communicatively coupled to a local server. For example, one orboth of vehicle bays 141 may be equipped with a respective storagedevice, which may be communicatively coupled to local server 121.Similarly, vehicle bay 142 may be equipped with a storage device, andthe storage device may be communicatively coupled to local server 122.In general, a vehicle bay may serve as a power station, a data transferstation, or both. In some embodiments, AHF system 100 may also include avehicle bay that serves neither as a power station nor as a datatransfer station. Instead, the specific vehicle bay may only serve as aparking station, providing ground robots or UAVs a safe place to park inthe horticultural field between executing missions. A vehicle bay may beprovided with an enclosing device such as a door, a cover, or a ceiling.The enclosing device may close to shield ground robots or UAVs docked inthe vehicle bay from weather or other external disturbance. When aground robot or UAV needs to enter or leave the vehicle bay, theenclosing device may open to provide a passage for a docked ground robotto go airborne or for an airborne ground robot to dock. Additionally,vehicle bay 142 may itself form a mesh network. In some embodiments, amesh network of bays can be used to extend range indefinitely, which canserve UAVs or UGVs in their vicinity.

For embodiments where AHF system 100 does not include directcommunication links between field sensors 111 and local server 121, orbetween field sensors 112 and local server 122, missions may be assignedto ground robots 131 and 132 and UAVs 133 to collect horticultural datafrom one or more sensors of field sensors 111 and 112. For example,field sensors 112 of greenhouse G02 may operate upon opto-electricalpower sources, without being communicatively coupled to local server 122via direct communication links. AHF system 100 may thus utilize groundrobots and UAVs to collect horticultural data from field sensors 112. Amission may dictate a ground robot of ground robots 131 and 132 and UAVs133 to travel to a vicinity of one or more sensors of field sensors 112deployed in greenhouse G02. The mission may further dictate the groundrobot or UAV to collect horticultural data, with time stamps, from theone or more sensors of field sensors 112 using various wirelesscommunication techniques between the sensors and the ground robot, suchas Wi-Fi, Bluetooth, Zigbee, infrared, or other low-power/short-rangewireless communication technologies. The horticultural data maytemporarily be stored in an on-board memory the ground robot is equippedwith. As the ground robot or UAV approaches one of vehicle bay 142, ordocked therein, the horticultural data may be uploaded or otherwisetransmitted to a storage device provided at vehicle bay 142. Thehorticultural data may be further uploaded to local server 122 via acommunication link between the storage device of vehicle bay 142 andlocal server 122.

In addition to field sensors 111 and 112, AHF system 100 may employ aplurality of cameras, such as still image cameras and/or video cameras,to collect horticultural data. For example, cameras 151 may bestrategically placed in field F01 to monitor grow operations 101 and 102of field F01. Cameras 151 may take still images or video recordings of aspecific area of grow operations 101 and 102 as horticultural data forAHF system 100. Likewise, cameras 152 may be strategically placed ingreenhouse G02 to monitor grow operation 103. Cameras 152 may take stillimages or video recordings of a specific area of grow operation 103 ashorticultural data for AHF system 100. Cameras 151 and 152 may becommunicatively coupled to local servers 121 and 122, respectively, sothat the still images and/or video recordings may correspondingly beuploaded to local servers 121 and 122. In some embodiments, cameras 151and 152 may have low light or night vision capabilities for monitoringfield F01 and greenhouse G02 during dawn and dusk hours, at night, orunder a low illumination condition. The still images and videorecordings captured by cameras 151 and 152 may be used or otherwiseanalyzed to provide horticultural data such as an estimate height, anestimated density of flower buds or fruits, an estimated size orquantity of produces, etc., regarding a grow operation or a specificplant thereof.

In some embodiments, AHF system 100 may include ground robots or UAVsthat are equipped with various cameras and sensors. Horticultural datamay thus be sensed or otherwise captured directly by the onboard camerasand sensors of the ground robots or UAVs as the ground robots traversethe horticultural field to different grow operations thereof. Strictlyspeaking, the use of sensing to obtain horticultural data by onboardcameras and sensors of ground robots, which may be referred as an“onboard-sensing” approach, may be exclusively employed by AHF system100 without resorting to the “field-sensing” approach described earlier,i.e., sensing to obtain horticultural data by field sensors 111 and 112as described above. Nevertheless, “onboard sensing” and “field sensing”can be mutually complimentary and may both be employed by AHF system 100to work in concert with one another.

In order for AHF system 100 to be capable of performing onboard sensing,one or more of ground robots 131 and 132 and UAVs 133 may be equippedwith at least a camera (e.g., a still image camera or a video camera) ora sensor (e.g., a thermometer, a hydrometer, a light meter, a barometer,an anemometer, or a pH meter). The camera may be used to capture apicture or a video of a grow operation as plant-related horticulturaldata, whereas sensors may be used to collect contextual information of agrow operation as non-plant-related horticultural data. Regarding theuse of an onboard camera, AHF system 100 may assign a mission, forinstance, to a ground robot 131 equipped with a camera to perform themission. The mission may contain an identification (e.g., a QR code, aserial number, an identification number, or a bar code) of a targetplant within field F01, as well as an action to be performed withrespect to the target plant. For example, the mission may instruct theground robot 131 to travel to a target plant of grow operation 102 tocapture pictures or a video recording of the target plant. The stillimages or the video recording may temporarily be stored in an onboardmemory of the ground robot 131 while the ground robot 131 is stilldeployed in the field, and subsequently uploaded to local server 121that is communicatively coupled to a vehicle bay 141 after the groundrobot 131 is docked in the vehicle bay 141. For some missions, theonboard camera may be able to provide certain horticultural data withoutactually recording or otherwise storing a picture or a video recording.For instance, the ground robot may monitor a grow operation with thecamera turned on, and an estimated height of the plants in the growoperation may therefore be estimated by the camera. Regarding usingonboard sensors, AHF system 100 may assign a mission, for instance, to aUAV 133 equipped with a light meter to perform the mission. The missionmay contain an identification of a target plant within greenhouse G02,as well as an action to be performed with respect to the target plant.For example, the mission may instruct UAV 133 equipped with a lightmeter to travel to a target plant of grow operation 103 to measure anillumination reading in a vicinity of the target plant. The illuminationreading may temporarily be stored in an onboard memory of the UAV 133 asthe UAV 133 is still deployed, and subsequently uploaded to local server122 that is communicatively coupled to vehicle bay 142 after the UAV 133is docked in vehicle bay 142. Alternatively, the illumination readingmay be readily transmitted, via a wireless means, to a storage device ofvehicle bay 142 while the UAV 133 is still deployed. In someembodiments, the illumination reading may be transmitted directly tolocal server 122 before the UAV 133 is docked in a vehicle bay such asvehicle bay 142.

In some embodiments, horticultural data may include a physical sample ofa plant. A ground robot may be equipped with a robotic arm capable oftaking a physical sample of a plant, such as a few leaves, some grains,or a fruit, of the plant. The ground robot may also be equipped with asample container for storing the sample. After a physical sample isacquired from the plant (e.g., a specific plant of grow operation 101)by the robotic arm and placed in the sample container, the ground robotmay transport the physical sample to a master grower onsite (e.g.,master grower 195 who is working in field F01) so that the master growermay examine the physical sample. In some embodiments, a ground robot maybe equipped with a sample container, but without a robotic arm capableof taking a physical sample of a plant. The sample may instead beacquired by a worker in the field (e.g., field worker 191). The fieldworker may carry a handheld communication device (e.g., personalcommunication device 193), via which the field worker may be informed ofwhat physical sample is to be acquired from which plant.

With an exception of a physical sample, horticultural data, after beingcollected from field F01 or greenhouse G02, may be stored in localserver 121 or 122, respectively, and subsequently be utilized by AHFsystem 100 in the AHF process. For example, the horticultural data maybe examined or otherwise analyzed by a human master grower, such asmaster grower 198, to identify various horticultural problems pertinentto the grow operations. As shown in FIG. 1, master grower 198 may not beonsite. That is, master grower 198 may be located remotely from fieldF01 and greenhouse G02, and horticultural data that has been uploadedand stored in local servers 121 and 122 may be accessed by master grower198 remotely. Master grower 198 may access the horticultural data viadata communication network 196. In some embodiments, horticultural datastored in local servers 121 and 122 may be transmitted to central server199 via network 196 for further processing or analysis.

Data communication network 196 may comprise a local area network (LAN),a wide area network (WAN), a mobile network, an Internet, or acombination of two or more thereof. The horticultural data may bepresented to master grower 198 via a user device 197. The user device197 may be a laptop computer, smartphone, desktop computer, tablet, orany other computing device. The user device 197 can be connected tolocal systems or cloud-based systems.

In some embodiments, the user device 197 may be used to define theboundaries of local areas/fields by placing markers on a map. In oneexample, a grower may open and execute a mobile application and submit arequest to register a new growing area or operation. The grower may usethe mobile application to place marker on a map, defining the boundariesof the growing area as an n-sided polygon, or some regular shape such asa circle, square, etc. By using the user device 197 to define theboundaries, the need for manually placing boundary markers aroundfields/growing areas may be eliminated, thus making the describedtechniques more scalable. The identification of local areas/fields canalso be automated using machine learning, thereby reducing oreliminating the need for the master grower to define boundaries.Additionally, when a grower is using a mobile phone/tablet to traversethe growing operation, the mobile application may automaticallydetermine the identification of in-proximity fields. For example, thegrower may approach a field in a large area of land. The mobileapplication can provide an indication that the grower is approaching anidentified field so that the grower can know which specific field isbeing approached. This can provide useful guidance during the plantingprocess.

In some embodiments, at least part of the horticultural data stored inlocal server 121 or 122 may be rendered or otherwise processed byfunctions that implement one or more algorithms before being presentedto master grower 198. For example, grow operation 101 of field F01 maybe growing cabbage. Horticultural data pertinent to grow operation 101,as stored in local server 121, may include pictures of grow operation101. An image processing algorithm may process the pictures and renderthe pictures to indicate by highlights some cabbage plants of growoperation 101 that may be showing yellowish leaves. Master grower 198may access the pictures with the highlights and identify a potentialhorticultural problem pertinent to the cabbage plants having yellowishleaves. As another example, grow operation 103 of greenhouse G02 may begrowing roses, which may be in a growing phase of producing flower buds.Horticultural data pertinent to grow operation 103, as stored in localserver 122, may include a first video recording of grow operation 103recorded on a first date, as well as a second video recording of growoperation 103 recorded on a second date that may be a few days after thefirst date. An image processing algorithm may compare the first andsecond video recordings and indicate with indications in the videorecordings some rose plants of grow operation 103 that may be producingsignificantly fewer flower buds as compared to other rose plants of growoperation 103. Master grower 198 may access the video recordings havingthe indications and identify a potential horticultural problem pertinentto the rose plants producing fewer flower buds.

In either example above, master grower 198 may not need to access andexamine all “raw data”, i.e., horticultural data as obtained and storedin local server 121 or 122, which may have been a time-consuming task,not to mention network 196 would have been heavily loaded and becomeinefficient. Instead, the software algorithms, through processing theraw data, help to direct the attention of master grower 198 to thoseplants that may have a higher probability of having a horticulturalproblem. Due to the scarcity of horticultural experts available, mastergrower 198 could have been a bottleneck of a horticultural feedbackprocess in a traditional approach. The bottleneck may be relieved in AHFsystem 100 thanks to the employment of the software algorithmsprocessing raw horticultural data. Certain computation power of acomputer may be needed for running or otherwise executing the softwarealgorithms on the computer. In some embodiments, local server 121 or 122may be taking the computation burden. That is, the software algorithmsmay be running on local server 121 or 122, where the raw horticulturaldata is readily available. In some embodiments, AHF system 100 may shiftthe computation burden to a central server 199, which may be morepowerful than local servers 121 or 122 in terms of running the softwarealgorithms more efficiently. That is, central server 199 may access theraw horticultural data stored in local servers 121 and 122, process theraw horticultural data by running the software algorithms, and save theprocessing result in central server 199. Master grower 198 may thusexamine the processing result on user device 197 by accessing theprocessing result stored in in central server 199 via network 196. Insome embodiments, central server 199 may duplicate the raw horticulturaldata stored in local servers 121 and 122. The duplicated copy may besaved in a storage device of central server 199 as a backup copy, incase the original copy of the raw horticultural data stored in localserver 121 or 122 is somehow lost, deleted, or damaged.

Based on the horticultural data, be it raw or software rendered, mastergrower 198 may identify various horticultural problems that need to beaddressed in field F01 and/or greenhouse G02. Furthermore, master grower198 may prescribe or otherwise determine a remedial solution (i.e., aremedial course of action) to address or mitigate the horticulturalproblems. For example, based on the horticultural data stored in localserver 121 and pertinent to grow operation 102 of field F01, mastergrower 198 may identify that specific cabbage plants of grow operation102 may have yellowish leaves, indicating a horticultural problem of thecabbage plants, as the yellowish leaves may be an indication of thecabbage plants not being healthy. Master grower 198 may determine thatthe cabbage plants need more water irrigation to address thishorticultural problem. Master grower 198 may assign to local server 121,via network 196, a remedial solution which, upon being executed, mayaddress the horticultural problem. Specifically, the remedial solutionmay be an extra twenty minutes of irrigation per day for the cabbageplants for a week. Local server 121 may command a robot deployed infield F01, such as irrigation robot 161, to carry out the remedialsolution. Irrigation robot 161 may locate in field F01 the cabbageplants that need more water, and then irrigate them according to theremedial solution prescribed by master grower 198. As another example,based on the horticultural data stored in local server 122 and pertinentto grow operation 103 of greenhouse G02, master grower 198 may identifythat specific rose plants of grow operation 103 may be producing fewernumber of rose buds than expected, a horticultural problem of the roseplants. Master grower 198 may determine that the rose plants need toreceive more illumination to address this horticultural problem. Mastergrower 198 may assign to local server 122, via network 196, a remedialsolution which, upon being executed, may address the horticulturalproblem. Specifically, the remedial solution may be an additional fourhours of illumination per day for the rose plants. Local server 122 maycommand an illumination device deployed in greenhouse G02, such asillumination device 162, to carry out the remedial solution.Illumination device 162 may locate in greenhouse G02 the rose plantsthat need more illumination, and then provide them with additionalillumination according to the remedial solution prescribed by mastergrower 198. In an event that illumination device 162 is not configuredto be controlled directly by local server 122, a field worker 192 mayoperate illumination device 162 manually to provide the additionalillumination. Local server 122 may wirelessly transmit the remedialsolution to a personal communication device 194 carried by field worker192 so that field worker 192 may be informed about the remedialsolution. Alternatively, personal communication device 194 may beconnected with network 196. Master grower 198 may prescribe the remedialsolution via user device 197, and the remedial solution may betransmitted to personal communication device 194 via network 196 so thatfield worker 192 may be informed about the remedial solution.

In various embodiments, AHF system 100 may have horticultural dataexamined or analyzed, horticultural problems identified, andcorresponding remedial solutions determined, all without a human mastergrower, e.g., master grower 198. Central server 199 may, for example, beinclude AI functions configured to analyze horticultural data, identifyhorticultural problems, and determine corresponding remedial solutions.Horticultural data stored in local servers 121 and 122 may betransmitted to central server 199 to be analyzed by the AI functions ofcentral server 199. In some embodiments, the AI functions of centralserver 199 may work in concert with master grower 198. For example, theAI functions may deal with routine horticultural problems, whereas humanmaster grower 198 may deal with horticultural problems that are moreadvanced, complicated, or uncommon.

Additionally or alternatively, AI functions may be built into a localserver, such as local servers 121 and 122. AI functions in a localserver may provide analysis, diagnosis, and remedial solutions in a waythat is more specific to the respective horticultural field, because theAI functions have been trained using horticultural data collected fromthe horticultural field. The use of local servers can also reducenetwork overhead, for example due to downloading of neural networks orother machine learning models. In some embodiments, training may beperformed off-site or in the cloud. Data incorporated from differentgrowing operations can be used to increase the performance of artificialintelligence (AI) models.

After a remedial solution is applied as prescribed, AHF system 100 mayconclude the AHF process by further monitoring the plants that have beentreated according to the remedial solution, so that an effectiveness ofthe remedial solution may be assessed. Similar to the collection ofhorticultural data before a horticultural problem is identified,horticultural data of plants after the remedial solution has beenapplied to the plants may be collected from field F01 or greenhouse G02using one or more of ground robots 131 or 132, possibly in conjunctionwith field sensors 111 or 112, as described above.

FIG. 1 also illustrates cloud-based resources 180 that is connected tonetwork 196. Some or all of the functionality described above withregard to FIG. 1 may be implemented in the cloud-based resources 180. Insome embodiments, cloud-based resources 180 may provide computing andstorage resources on-demand and as needed. In other embodiments, most orall of the storage and computing functions may be performed usingcloud-based resources 180. In this case, local server 121, 122 andcentral server 199 may not be utilized. In some embodiments,functionality may be distributed between cloud-based resources 180 andon-site resources.

When traversing a horticultural field to perform various AHF missions, aground robot is required to position itself within the horticulturalfield so that the ground robot may navigate while traversing thehorticultural field. In some embodiments, the positioning/navigationfunction may be realized by a global positioning system (GPS) receiverdisposed on the ground robot. For example, each of ground robots 131 and132 may be equipped with such a GPS receiver. The GPS receiver of therobot may receive positioning signals from a plurality of space-basedsatellites. The GPS receiver may further triangulate the positioningsignals to determine a three-dimensional (3-D) geophysical position ofthe robot on the Earth surface.

The effectiveness of a GPS receiver may be compromised if the receptionof the satellite-originated positioning signals is less than ideal. Thequality of reception of the positioning signals may be affected byweather, electromagnetic interference/shielding, or physical blocking.For example, whereas a ground robot 131 serving the open field F01 mayreceive satellite signals most of the time, a ground robot 132 servingthe enclosed greenhouse G02 may at times experience difficultiesdetermining its position using GPS, as the building structure ofgreenhouse G02 may block or at least greatly attenuate the GPS satellitesignals. Therefore, positioning mechanisms other than using a GPS, suchas the positioning mechanism illustrated in FIG. 2, may be provided toan AHF system such as AHF system 100.

FIG. 2 illustrates an example positioning mechanism 200 that isapplicable to AHF system 100 in an implementation with ground robots.For the purpose of positioning a robot within a horticultural field, thehorticultural field is often divided into a plurality of local areas,which are smaller in size. In general, a horticultural field may bedivided into local areas that are similar in respective size, and thelocal areas may collectively form a matrix. In some embodiments,however, a horticultural field may be divided into local areas ofvarious sizes, especially if the shape of the horticultural field isirregular. A horticultural field may be divided into local areas usingradio beacons disposed in the horticultural field. The beacons emitradio signals that enable a ground robot receiving the signals todetermine its location relative to the beacons. Beacon signals can, forexample, uniquely identify their source beacon, indicate location (e.g.,coordinates) of the beacon emitting them, indicate a direction to thebeacon emitting them, indicate a degree or standard of power, and soforth.

As shown in FIG. 2, horticultural field F03 is divided into nine localareas that collectively form a 3×3 matrix. View 291 illustrates a 3-Dperspective view of field F03, whereas view 299 illustrates a top viewof a portion of field F03. A plurality of beacons 211 may be disposedacross field F03 to define the local areas of field F03. The local areasof field F03 are referred in FIG. 2 as F03-A1, F03-A2, F03-A3, F03-B1,F03-B2, F03-B3, F03-C1, F03-C2, and F03-C3, respectively. Although eachof beacons 211 may be physically identical, each of the plurality ofbeacons 211 may emit a respective beacon signal, i.e., aself-identifying radio signal. Refer to local area F03-A1 of FIG. 2,which is largely of a rectangular shape, as an example. A respectivebeacon 211 is disposed at each of the four corners of local area F03-A1,and is emitting a beacon signal. As shown in both view 291 and view 299,the four beacons disposed at the corners of local area F03-A1 areemitting beacon signals 221, 222, 224 and 225, respectively. Also shownin view 291 and view 299 are two other beacon signals 223 and 226, whichare being emitted from the two beacons 211 that are disposed at the twocorners of local area F03-B1 that are not neighboring local area F03-A1.Beacon signals 221-226 may be encoded in respectively unique radiopatterns so that they are self-identifying. When a beacon signal emittedfrom a specific beacon 211 is received by an antenna, the radio patternembedded in the beacon signal may uniquely reveal which beacon 211 thebeacon signal is emitted from.

The self-identifying radio signals emitted from beacons 211, such asbeacon signals 221-226, may be utilized by a ground robot 230 (depictedin the figure as an autonomous vehicle or “AV”) to identify the locationof ground robot 230 within field F03 as ground robot 230 traverses fieldF03. Ground robot 230 may use various radio-based trilaterationtechniques for positioning. In some embodiments, the self-identifyingradio signals, including beacon signals 221-226, may be emitted frombeacons 211 with a constant signal strength. That is, each of the beaconsignals may exhibit the same signal strength at the transmitting end,i.e., at a respective beacon 211. Since signal strength of a radiosignal continues to decay as the radio signal travels further away fromits origin, ground robot 230 may translate the strengths of the beaconsignals, as received by ground robot 230 at its immediate position, intocorresponding distances between ground robot 230 and beacons 211, atleast in relative terms. The position of ground robot 230 within fieldF03 may accordingly be determined or otherwise inferred based on thedistances by interpolation or extrapolation. For example, ground robot230 may be traversing field F03 along a path 240 while constantlyreceiving radio signals 221-226 emitted from beacons 211. Let S231denote the signal strength of beacon signal 221 as received by groundrobot 230 at an immediate position of ground robot 230. Also let S222,S223, S224, S225 and S226 denote the signal strengths of beacon signals222, 223, 224, 225 and 226 as received by ground robot 230 at itsimmediate position, respectively. In response to a condition where S221,S222, S224 and S225 are substantially the same, ground robot 230 mayinfer that its immediate location is at or around location 251 withinfield F03, i.e., around a center location of local area F03-A1. Inresponse to a condition where S222, S223, S225 and S226 aresubstantially the same, ground robot 230 may infer that its immediatelocation is at or around location 253 within field F03, i.e., around acenter location of local area F03-B1. In response to a condition whereS221, S223, S224 and S226 are substantially the same, ground robot 230may infer that its immediate location is at or around location 252within field F03, i.e., around a center location of a contiguous areaformed by local areas F03-A1 and F03-B1.

In addition to positioning, the beacon signals emitted by beacons 211may also be utilized for navigation. For example, ground robot 230 maybe currently at location 253, and S222, S223, S225 and S226 aresubstantially the same. In order to continue moving along path 240toward location 254, ground robot 230 may move incrementally toward adirection so that S225 and S226 increases at a same rate while S222 andS223 decreases at a same rate.

In some embodiments, ground robot 230 may position or navigate withoutbeacon signals of a constant signal strength being emitted by beacons211. Specifically, beacons 211 may emit beacon signals in a synchronizedmanner, wherein the beacon signals are not required to have a samesignal strength when leaving beacons 211. ground robot 230 may positionand navigate within field F03 not based on signal strengths of thebeacon signals as received, but based on propagation delays of thebeacon signals. A propagation delay of a beacon signal is defined by thetime the beacon signal takes to arrive at the immediate location ofground robot 230 after being sent from a respective beacon 211.

Since the division of a horticultural field into local areas is based onthe radio signals of beacons, the boundaries of the local areas areimaginary, and may not stay fixed from an administration point of view.Based on specific horticultural needs, the number and boundaries oflocal areas of a horticultural field may be changed, usually betweenhorticultural seasons. The beacons may be re-arranged, with or withoutan increase or decrease in a total number of beacons, to divide ahorticultural field into local areas in a different way as compared to aprevious horticultural season. In some embodiments, QR codes that arevisible from the UAV/UGV may also be used.

An AHF system may include an administrative scheme, which is a plan formaintaining a database comprising various information items thatcollectively reflect a status of one or more horticultural fieldsadministrated by the AHF system. Each of FIGS. 3-8 and 10-12 illustratesan information item of the administrative scheme, as described below indetail. The administrative scheme marshals, in a real-time and/orjust-in-time manner, various status or information of a horticulturalfield during an AHF process performed by the AHF system. Specifically,for each horticultural field administrated by the AHF system, theadministrative scheme may facilitate real-time or just-in-timemarshaling of information regarding AHF activities. Thereal-time/just-in-time information may include but is not limited to:(1) names of plant, locations, and growing phases of various growoperations currently growing in the horticultural field; (2) respectivelocations of individual plants within a grow operation; (3) zones withinthe horticultural field in which ground robots are operable ornon-operable; (4) status of ground robots servicing the horticulturalfield; and (5) status of past AHF missions, current AHF missions, andplanned AHF missions.

FIG. 3 shows an example operation dashboard 300 as an information itemof the administrative scheme of AHF system 100. According to operationdashboard 300, AHF system 100 is currently administrating fourhorticultural fields: F01, G02, F03, and F04. Each of the fourhorticultural fields is growing one or more grow operations, and eachgrow operation is uniquely identified by an operation ID in operationdashboard 300. For example, horticultural field F04 is growing fourdifferent grow operations: op120, op222, op512, and op664. Operationdashboard 300 also records, for each grow operation, the name of thecrop or plant that is growing, as well as a respective growing phase ofthe crop or plant at the moment. For instance, operation dashboard 300records that operation op512 is a grow operation growing yellow corn,and is currently in growing phase 1. Operation dashboard 300 also showsthat operation op222 has cabbage in growing phase 4. The “growing phase1” information regarding operation op512 may indicate that the cornplants of operation op512 were planted just recently, whereas the“growing phase 4” information regarding operation op222 may indicatethat the cabbage plants of operation op222 are almost ready to beharvested. More information regarding each grow operation may beincluded in operation dashboard 300, such as a start date and anestimated harvest date of the grow operation, an acreage of the growoperation, various horticultural substances (e.g., fertilizer,pesticide) that have been applied to the grow operation, and so forth.

The administrative scheme of AHF system 100 may include a local area mapfor each horticultural field, as each horticultural field may be dividedinto a plurality of local areas using beacons or QR codes, asexemplified in FIG. 2. The local area map may specify a uniqueidentifier for each of the local areas of the horticultural field. FIG.4 illustrates a local area map 400 of field F04, as another informationitem of the administrative scheme of AHF system 100. As shown on localarea map 400, field F04 is divided as a 7×7 matrix having forty-ninelocal areas, each identified with a respective identifier. For example,the seven local areas in the first column of the matrix are specifiedwith identifiers A1, A2, A3, A4, A5, A6, and A7, respectively, whereinthe seven local areas in the middle row of the matrix are specified withidentifiers A4, B4, C4, D4, E4, F4, and G4, respectively.

The administrative scheme of AHF system 100 may include a physicalstructure map for each horticultural field. The physical structure mapmay illustrate or otherwise record locations of various physicalstructures or objects in the horticultural field. AHF system 100 mayrefer to the physical structure map for various administrative purposes.For example, AHF system 100 may refer to the physical structure map whenmoving grow operations within the horticultural field, or when assigninghorticultural missions to ground robots. Publicly available 3D data canbe used to define the structure map in order to help automate thisprocess.

FIG. 5 illustrates a physical structure map 500 of field F04, as anotherinformation item of the administrative scheme of AHF system 100. Asshown on physical structure map 500 and with reference to local area map400, field F04 has a road 510 extending from local area A3 to local areaF3, as well as a road 520 extending from local area F3 to local area F7.The roads 510 and 520 may be used for ground traffic of horticulturalvehicles (e.g., trailer, tractors, or trucks), and may not be used aspart of a grow operation. Additionally, physical structure map 500 alsoshows that field F04 has a water tower 530 in local area G7, an electrictower 540 in local area F1, and two ground robot bays (i.e., vehiclebays for ground robot) vb01 and vb02 located in local area A7 and localarea G3, respectively. Local areas occupied by the various physicalstructures (e.g., roads 510 and 520, water tower 530, electric tower540, ground robot bays vb01 and vb02) may not be available as part of agrow operation, at least not completely available. Some physicalstructures, however, may permit land usage for growing plants. Forexample, electrical power cables in an area 550 may pass through thelocal area G1 in the air, but would still allow local area G1 to be usedas part of a grow operation.

An area 552 that is occupied by electrical power towers and cables isalso specified on physical structure map 500. Even though area 552 isnot officially within the boundaries of field F04, the proximity of theelectrical power cables in area 552 may interfere or otherwise affectcertain horticultural activities performed within field F04. Forexample, to avoid interference with electrical power cables in the area552, ground robots traversing across field F04 near the area 550(especially over local areas A1, B1, C1, D1, E1) can maneuver in zonessafely avoiding the electrical towers and power cables.

The administrative scheme of AHF system 100 may include a grow operationmap for each horticultural field. The grow operation map may show orotherwise indicate which local areas of a horticultural field are beingoccupied by which grow operations. FIG. 6 illustrates a grow operationmap 600 of field F04, as another information item of the administrativescheme of AHF system 100. As shown on grow operation map 600, growoperation op120 is taking up local areas A5-A7 and B5-B7; grow operationop222 is taking up local areas D5-D7, E5 and E6; grow operation op512 istaking up local areas A1, A2, B1 and B2; grow operation op664 is takingup local areas G1, G2 and G4-G6. Combining grow operation map 600 andoperation dashboard 300, a utilization of Field 04 may becomprehensively presented. In some embodiments, information contained ingrow operation map 600 may be integrated into operation dashboard 300.

The administrative scheme of AHF system 100 may include a field activitymap for each horticultural field. The field activity map may show orotherwise indicate various horticultural activities scheduled to happenwithin a span of time, for instance, one day. Some of the horticulturalactivities may direct to a grow operation currently growing in thehorticultural field. Some of the horticultural activities may bedirected to a grow operation that has not started growing, or a growoperation that has recently been harvested. FIG. 7 illustrates a fieldactivity map 700 of field F04 for the day of May 22^(nd), as anotherinformation item of the administrative scheme of AHF system 100. Asshown on field activity map 700, on May 22^(nd), soil plowing activitieswill be conducted in local areas C1 and C2 of Field F04. Additionally,harvesting will be conducted in local areas D5-D7, and post-harvestclean-up activities will be conducted in local areas E5 and E6.

When a ground robot traverses a horticultural field, it is imperativethat the ground robot avoids certain restricted zones (RZs) andobstructions identified by the administrative scheme. In general, an RZmay include a portion of a local area bounded by a geometric shape suchas a rectangle. An RZ may also include portions of several local areaswherein the portions are continuous. Ground robots would want to avoidthe RZs to ensure safety and regulation observance. Ground robots mayrefrain from entering RZs so that they do not run into various physicalstructures on the horticultural field. Additionally, the RZ may includeidentification of local areas that include specific obstructions thatmay be identified, for example, using geographic coordinates or othermeans for identifying a location.

Ground robots may refrain from entering RZs so that they may notinterfere with horticultural activities that may obstruct ground robotmovement. Ground robots may refrain from moving through certain areas toavoid interfering with the plants of the grow operation, wherein the RZmay be determined based on an estimated perimeter of the canopy of theplants of the grow operation, plus some safety or clearance margin.Space reserved for ground traffic, or around an area reserved for foottraffic, may be identified as an RZ. Depending on the weather (e.g.,wind gust, lightning, hail), certain areas of the horticultural fieldmay be adversely affected, and those areas may be identified as RZs. Inaddition, government regulations may forbid operation of ground robotsin certain areas, and those areas may also be identified as RZs by theadministrative scheme of the AHF system.

FIG. 8 illustrates an RZ map 800 of field F04 for the day of May22^(nd), as another information item of the administrative scheme of AHFsystem 100. As shown on RZ map 800, restrictions, if any, are specifiedfor each local area of field F04. For example, the administrative schemeof AHF system 100 may limit ground robots entering a local area reservedfor ground traffic so that ground robots may not interfere with tractorsor other ground vehicles that may be in the ground traffic. Therefore,RZ map 800 may specify that local area F1 is an RZ.

Likewise, other physical obstructions on or near field F04 may dictatesome RZs on RZ map 800. For example, an obstruction may be identified inlocal area C3, and another physical obstruction may be identified inlocal area F5. In determining a path for a ground robot, RZs on RZ map800 are to be observed and avoided.

FIG. 9 illustrates an example path using the example of unmanned aerialvehicles (UAVs). FIG. 9 illustrate an example path 910 for a UAV 920, asdetermined by RAHF system 100, after UAV 920 is assigned a horticulturalmission to collect certain horticultural data regarding a target plant930 of field F04. The mission may comprise collecting a pH level readingof the soil that grows target plant 930. When the mission is assigned,UAV 920 may be docked in UAV bay vb01, which is located in local area A7according to physical structure map 500. The target plant 930 may belocated in local area B1 of field F04. Also shown in FIG. 9 are NFZs941, 942, 943, 944, 945 and 946, which are consistent with the altitudelimits specified in NFZ map 800. Specifically, path 910 avoids NFZs941-946. It shall be noted that path 910's avoiding NFZs 941-946 doesnot mean path 910 is completely exclusive from NFZs 941-946 in all thelocal areas path 910 intersects with. In fact, path 910 may enter an NFZin a local area where it starts, and path 910 may enter an NFZ in alocal area where it ends. However, path 910 does not intersect with anNFZ when traveling through local areas in between. Traveling along path910, UAV 920 may originate from UAV bay vb01 in local area A7, passsequentially through local areas A6, A5, A4, B4, B3, B2 along path 910,and arrive at target plant 930 in local area B1. Since the height of UAVbay vb01 is typically below the flying altitude limit set by an NFZ, itis obvious that UAV 920 would be in NFZ 941 when leaving UAV bay vb01.Also, for UAV 920 to collect the pH level reading from pH meter 950embedded in the soil growing target plant 930, it is obvious that UAV920 has to be within a wireless communication range from pH meter 950,which may require UAV 920 to enter NFZ 945 when in local area B1.Nevertheless, UAV 920 does not enter any of the NFZs 941-946 whenpassing through local areas A6, A5, A4, B4, B3, and B2. For paths thatneither originate nor end in local area A7, such as path 913 for UAV923, NFZ 941 has to be observed and avoided. Likewise, for paths thatneither originate nor end in local area B1, such as path 916 for UAV926, NFZ 945 has to be observed and avoided.

When UAV 920 travels “along” path 910, UAV 920 may not be moving exactly“on” path 910 during the whole time of the traveling. Rather, when UAV920 travels along path 910, UAV 920 may be located close to path 910within a range of proximity 911. The range of proximity 911 may bedependent, at least partly, on how well UAV 920 may position andnavigate itself. External aviation factors, such as sidewind or localair vortex, may also affect the range of proximity 911.

An RZ map of a horticultural field, such as RZ map 800, may be updatedconstantly to reflect, in a real-time or just-in-time manner, RZ changesdue to weather change or weather forecast, crop growth, or horticulturalactivities happening in the horticultural field. For example, on May20^(th), cabbage plants of grow operation op222 are not yet harvested,and the May 20^(th) RZ map of field F04 may indicate that ground robotsare free to travel in local areas D5, D6, D7, E5, and E6. Namely, RZs inlocal areas D5, D6, D7, E5, and E6 include only space at 15 ft altitudeor below. On May 21^(st), cabbage plants in local areas E5 and E6 arebeing harvested, and the May 21^(st) RZ map may expand RZs in localareas E5 and E6 to accommodate the harvest activity. According to fieldactivity map 700, cabbage plants in local areas D5-D7 are to beharvested on May 22^(nd). Therefore, RZ map 800, of May 22^(nd),indicates that RZs in local areas D5-D7 are also added. Meanwhile, RZmap 800 indicates that RZs in local areas E5 and E6 are added as thehorticultural activity in local areas E5 and E6 on May 22^(nd) would bepost-harvest cleaning according to field activity map 700. Likewise,even though local areas C1 and C2 are not currently growing a growoperation, an RZ may be indicated on RZ map 800 for the two local areas.The RZ in local areas C1 and C2 is identified so that ground robots maynot interfere with a horticultural activity of soil plowing, which isindicated on field activity map 700.

The update rate of RZ map 800 may be as frequent as every minute or moreoften, so that RZ map 800 is essentially true in a real-time sense. Themost recent version of RZ map 800 may be sent to every ground robot inservice, especially to those that are deployed for missions, so that theground robots may avoid all RZs on RZ map 800, including the mostrecently updated ones, when traversing field F04. In some embodiments,information regarding the RZs of RZ map 800 may be saved into an RZlist, which may be transmitted to every ground robot in service. The RZlist may essentially include the same information as represented by RZmap 800. Each ground robot in service may store a copy of the RZ listonboard for reference by a navigation module of the respective groundrobot. As RZ map 800 gets changed, the associated RZ list may be updatedaccordingly and transmitted to ground robots. A ground robot mayaccordingly change or otherwise update its planned path to conform tothe updated RZ list.

In some embodiments, an update to an RZ map may be triggered by camerasdeployed in the horticultural field. For example, an RZ map regardingfield F01 of FIG. 1 may be triggered by any of cameras 151, and an RZmap regarding greenhouse G02 of FIG. 1 may be trigger by any of cameras152. Cameras 151 and 152 may observe various horticultural activities,planned and unplanned, in field F01 and greenhouse G02, and trigger anRZ update should an activity may interfere with ground robot operation.For example, a camera 151 may observe that irrigation robot 161 has beendeployed unexpectedly (e.g., not as planned according to a fieldactivity map of field F01), which may impede ground robot movement incertain local areas. The RZ map may be updated such that the affectedspace is included in the RZs. Ground robots servicing field F01 mayreceive an updated RZ list, and adjust respective travel paths to avoidthe local areas affected by the unexpected operation of irrigation robot161. In some embodiments, autonomous vehicles with on-board vision maybe able to avoid smaller obstacles such as deployed robots. Thegeneration of the RZ can just be adjusted based on the capabilities ofthe autonomous vehicles.

Likewise, field sensors may also trigger an RZ update. For example,anemometers deployed in field F04 may sense a wind gust at 20:00 of May22^(nd), and AHF system 100 may determine that the wind gust is toostrong for ground robots av02, av03, av04 and av08 to operate safely incertain local areas of field F04. AHF system 100 may update RZ map 800accordingly, at least for ground robots av02, av03, av04 and av08, whichmay each receive an updated RZ list. Each of ground robots av03, av04and av08, while deployed, may change its respective travel path based onthe updated RZ list. In some embodiments, a field sensor may trigger atemporary hold of all the ground robot operations for a horticulturalfield. For example, field sensors 112 deployed in greenhouse G02 mayinclude an earthquake detector. Upon the earthquake detector sensing anearthquake of a significant scale, AHF system 100 may determine toimmobilize all ground robots servicing greenhouse G02 until theearthquake subsides. AHF system 100 may cause the ground robots toimmobilize by updating an RZ map of greenhouse G02 to include all localareas of greenhouse G02. Alternatively, AHF system 100 may directlyissue an emergency immobilization command to cause all deployed groundrobots in greenhouse G02 to suspend all movement immediately, instead ofupdating the RZ map and sending updated RZ lists to ground robots.

In some embodiments, not all ground robots in service may share the sameRZ list. Namely, a ground robot servicing field F04 may have an RZ map800 containing RZ information tailored to the specific ground robot,whereas another ground robot servicing field F04 may have a different RZmap 800 containing different RZs. For example, ground robots may havedifferences in speed and maneuvering capabilities, safety margins, orother specifications. For example, a zone having certain terraincharacteristics may be traversed based on the ground robot capabilities,whereas the terrain may represent a safety concern to other groundrobots.

The administrative scheme may also facilitate a centralized dashboardshowing status of robots, including ground robots, being used in the AHFsystem. FIG. 10 shows an example ground robot dashboard 1000, which isanother information item of the administrative scheme of AHF system 100.ground robot dashboard 1000 reflects status of ground robots thatservice field F04, as of 20:00 on May 22^(nd). Similar to RZ map 800,ground robot dashboard 1000 may be frequently updated to provide areal-time/just-in-time effectiveness of the status. ground robotdashboard 1000 includes status of ten ground robots, av01-av10. For eachground robot thereof, ground robot dashboard 1000 records a currentstatus in general, an immediate location, a mission ID representing ahorticultural mission that has been assigned to the respective groundrobot, a fuel or battery level, whether the respective ground robot isavailable for a new mission assignment, various resources the respectivevehicle is equipped with (e.g., sensors, cameras, memory, samplecontainers, etc.), and other specifications (e.g., payload). As shown inground robot dashboard 1000, three out of the ten ground robots, i.e.,av03, av07 and av08 have been assigned a mission that is either being oryet to be executed. In some embodiments, a ground robot having anassigned mission may not be assigned another mission until the currentlyassigned mission has been completed or canceled. In some embodiments, aground robot may be assigned multiple missions, wherein the missions areenqueued for the ground robot to execute in sequence.

As shown in ground robot dashboard 1000, five out of the ten groundrobots listed in ground robot dashboard 1000 are currently unavailablefor a new mission assignment for various reasons. ground robot av03 isunavailable because it is currently deployed and has a missionassignment, mission m10080. Ground robot av05 is unavailable due to amechanical problem of the ground robot. ground robot av07, althoughalready docked in ground robot bay vb01, is unavailable because it istransferring horticultural data from mission m10073 that it justexecuted to a storage device of ground robot bay vb01. ground robot av06is unavailable because its battery charge level is too low. To prevent aground robot from running out of fuel or battery power in the middle ofexecuting a horticultural mission, AHF system 100 may impose a powerthreshold for the ground robots, which requires a ground robot to dockand replenish fuel or charge a battery to a level above the powerthreshold before the ground robot becomes available for a new mission.In some embodiments, the power threshold may be a range of values, so asto provide a hysteresis function in ground robot power management. Forexample, AHF system 100 may impose a power threshold of 10%-40%. Thatis, a ground robot is forced to dock and replenish fuel or charge abattery when the power level of the ground robot drops below 10%, andthe ground robot is not available to receive a new mission assignmentuntil the ground robot regains its power level over 40%. As shown inground robot dashboard 1000, ground robots av06 and av09 arerespectively docked in a vehicle bay and charging. Ground robot av09 isavailable for mission assignment because its current battery chargelevel, at 45%, is already higher than the power threshold (i.e.,10%-40%). In contrast, ground robot av06 is not yet available formission assignment because its current battery charge level, which is at20%, is still not higher than the minimum departure power threshold of40%. In some embodiments, AHF system 100 may impose a memory thresholdon the ground robots in a concept similar to a power threshold. Asdescribed above, each ground robot may be equipped with an onboardmemory device for temporarily storing horticultural data (e.g., picturesof a target plant or video of a grow operation). Therefore, the memorythreshold is imposed to prevent ground robots from running out ofonboard memory for storing horticultural data during a horticulturalmission, very much in a similar way the power threshold is imposed toprevent ground robots from running out of power during a horticulturalmission. For example, AHF system 100 may impose a memory threshold oftwo gigabytes (GB) of free or available memory. That is, a ground robotmay not be assigned a new mission unless the ground robot has at least 2GB of free memory. Accordingly, ground robot av10 is unavailable for amission because its onboard memory is too full, having only 0.3 GB left,which is less than the memory threshold (i.e., 2 GB of free memory).ground robot av10 may need to free up some of the onboard memory so thatit may have more than 2 GB of free memory, before ground robot av10 maybecome available to take on a new mission. ground robot av10 may do soby uploading some of the horticultural data currently stored in theonboard memory to a storage device of a vehicle bay, or directly to alocal server, through either wired or wireless means.

On the other hand, the other five ground robots listed in ground robotdashboard 1000 (i.e., ground robots av01, av02, av04, av08 and av09) areimmediately available for a new mission assignment. Ground robots av01,av02 and av09 are docked in vehicle bay vb01. Ground robot av04 may havejust finished another mission and is still flying, currently in localarea G6. Although airborne, ground robot av04 is also available for anew mission. ground robot av08, in some embodiments, may not beavailable for taking on a new assignment, as it may still betransmitting horticultural data collected from mission m10077 whiletraveling in local area D6. In some embodiments, however, ground robotav08, may be allowed to accept a new mission assignment, especially ifthe new mission involves a target plant that is in or around local areaE6. The horticultural data from mission m10077 may still be continuouslyuploaded until the upload is complete while ground robot av08 executesthe new mission.

The administrative scheme may also facilitate a centralized dashboardshowing status of horticultural missions of the AHF system. FIG. 11shows an example mission dashboard 1100, which is another informationitem of the administrative scheme of AHF system 100. Mission dashboard1100 reflects status of horticultural missions having been, being, oryet to be performed by ground robots in field F04, as of 20:00 on May22^(nd). Similar to RZ map 800 and ground robot dashboard 1000, missiondashboard 1100 may be frequently updated to provide areal-time/just-in-time effectiveness of the status of the missions. Sixexample missions are listed in mission dashboard 1100, each identifiedwith a unique mission ID, i.e., m10061, m10070, m10073, m10077, m10080,and m10091. Each mission includes a target located within field F04, aswell as an action to be performed with respect to the target. For somemissions (e.g., m10061 and m10070), the target may be a grow operationin its entirety. For some missions (e.g., m10073, m10077 and m10091),the target may be one or more local areas within a grow operation. Forsome missions (e.g., m10080), the target may be one or more specificplants within a local area. Mission dashboard 1100 also records, foreach mission, the action to be performed with respect to the target. Inmission dashboard 1100, each mission may have a respective missionstatus recorded as one of the following: “to be assigned”, “assigned”,“in progress”, or “completed”. A mission having a “to be assigned”status is a mission that has been entered or otherwise initiated intoAHF system 100, but has yet to be assigned to a ground robot. A missionhaving an “assigned” status is a mission that has been assigned to aground robot, but the execution of the mission by the ground robot hasnot yet started. A mission having an “in progress” status is a missionthe execution of which has been started. A mission having a “completed”status is a mission that has been completed. Among the missions listedin mission dashboard 1100, missions m10061 and m10070 have beencompleted, missions m10073, m10077 and m10080 are being executed,whereas mission m10091 has not been assigned to a ground robot.

Each horticultural mission in mission dashboard 1100 is respectivelyrecorded with an “entry time” and an “intended time window”. The entrytime of a mission is the time the mission is entered or otherwiseinitiated in AHF system 100. A mission may be entered or initiated bymaster grower 198, or the AI functions of central server 199. A missionfor collecting horticultural data may be pre-scheduled to monitorgrowing conditions of grow operations. A mission for implementing aremediation solution may be entered upon a possible horticultural issueis identified based on the horticultural data collected. The intendedtime window of a mission, which may be designated by master grower 198or the AI functions of central server 199 when the mission is entered,is a period of time during which the mission is intended to carry out.For example, according to mission dashboard 1100, mission m10061 wasentered at 13:00 on May 22^(nd), and was intended to be executed between15:00 and 17:00 on the same day.

In addition, mission dashboard 1100 also record which ground robot(s)each mission is assigned to. For example, mission m10061 was assignedto, and has been executed by, ground robot av01, whereas mission m10073is assigned to, and being executed by, ground robot av07. In someembodiments, a mission may not be assigned to a ground robot soon afterthe mission is entered. In fact, it may be preferred not to assign amission until a short time before the intended time window of themission. In some cases, a mission may even be assigned during theintended time window, as long as the mission can be completed within theintended time window. For example, mission m10073 is entered at 13:30 onMay 22^(nd) but not intended to be executed until some time between17:00 and 19:00 on the same day. Accordingly, AHF system 100 may notassign mission m10073 to a ground robot until a short time (e.g., 5 to10 minutes) before 17:00. Alternatively, AHF system 100 may not assignmission m10073 to a ground robot until after 17:00. By making short thetime difference between mission assignment and the intended time windowof the mission, the utilization of ground robots may be more efficient.For instance, this approach may make it more likely that the mission beassigned to a most suitable ground robot at the time the mission isintended to carry out. It may also avoid a situation where a groundrobot is tied up to a future mission and thus unavailable to a moreimmediate mission.

As stated above, a target of a mission may be a specific plant of a growoperation. The specific plant may be identified in an AHF system using aunique identification. For example, mission m10080 in mission dashboard1100 is intended for a target having an identification “pu_1231”, whichmay represent a unique plant in field F04. Specifically, mission m10080intends to collect a measurement reading of the pH level of the soil inwhich the unique plant represented by identification “pu_1231” isplanted. To this end, the administrative scheme of AHF system 100 mayinclude a plurality of plant unit (PU) lists, which make up anotherinformation item of the administrative scheme of AHF system 100. Each PUlist corresponds to a specific local area of a specific horticulturalfield, and records which PUs are contained in the local area. Therefore,by searching through the PU lists, AHF system 100 is able to determinewithin which local area of which field a specific PU is located.

In some embodiments, PUs may be individual planters (e.g., containersthat hold soil and plants), and each planter may grow a plant or severalplants. In some embodiments, PUs may not be physical planters, butsimply imaginary designations of plants for administrative purposes. Forexample, plants in a local area may be growing in rows or clusters butnot in physical planters, and each cluster or row of plants may bedesignated as a PU of the local area. Each PU is uniquely identified bya PU identification (hereinafter referred as a “PUID”) within AHF system100. Namely, a PU is uniquely identified by its PUID among all the PUsof all the horticultural fields managed by AHF system 100.

FIG. 12 illustrates a plurality of PU lists 1200 of field F04. Each ofPU lists 1200 corresponds to a local area of field F04. As shown inlocal area map 400, field F04 has a total number of forty-nine localareas, i.e., local areas A1, A2, A3, . . . , G5, G6, G7. Therefore, PUlists 1200 may include a total number of forty-nine lists, eachrespectively corresponding to one of the local areas of field F04.Specifically, PU lists 1200 includes a PU list for local area A1, andthe PU list, labeled as “PUL_F04_A1” in FIG. 12, records eight PUIDs,i.e., pu_1211, pu_1221, pu_1231, pu_1241, pu_1251, pu_1261, pu_1271 andpu_1281. Each of the eight PUIDs may uniquely represent a PU in localarea A1 of field F04. As shown in still image picture 1290 of local areaA1 of field F04, local area A1 includes a total number of eight PUs,i.e., PUs 1212, 1222, 1232, 1242, 1252, 1262, 1272 and 1282. Each of theeight PUs is identified by one of the eight PUIDs recorded in PU listPUL_F04_A1. Moreover, each of the eight PUs may be provided with one ofPU labels 1213, 1223, 1233, 1243, 1253, 1263, 1273 and 1283. Each of thePU labels may read or otherwise reveal the respective PUID of the PU towhich the PU label is provided. A PU label may be placed at a knownlocation on or around a PU (e.g., on the sidewall of a planter, or on astick next to the PU). In some embodiments, a PU label may be a visualmarker containing one or more visual codes, such as a barcode or a QRcode. In some embodiments, especially for horticultural missions in lowlight conditions, a PU label may be a radio frequency identification(RFID) label. In either way, a PU label provided at a PU is able toreveal the PUID of the PU when recognized by a camera or scanned by aradio frequency (RF) scanner. When a ground robot approaches the plantsin local area A1 of field F04, the ground robot may use a visual cameraor a RF scanner equipped in the ground robot to scan the PU labels andidentify the plants in the PUs. Specifically, upon the scanning, PUlabel 1213 attached to PU 1212 may reveal PUID pu_1211, which uniquelyidentifies PU 1212 in AHF system 100. Likewise, PU label 1223 attachedto PU 1222 may reveal PUID pu_1221 upon the scanning. PU label 1233attached to PU 1232 may reveal PUID pu_1231 upon the scanning. PU label1243 attached to PU 1242 may reveal PUID pu_1241 upon the scanning. PUlabel 1253 attached to PU 1252 may reveal PUID pu_1251 upon thescanning. PU label 1263 attached to PU 1262 may reveal PUID pu_1261 uponthe scanning. PU label 1273 attached to PU 1272 may reveal PUID pu_1271upon the scanning. PU label 1283 attached to PU 1282 may reveal PUIDpu_1281 upon the scanning. It should be understood that the partitioningof an area into a grid is provided as an example, and that thepartitioning of a field/local area into any shape may be implemented.

With the aid of the PU lists of the administrative scheme and the PUlabels physically disposed in the field, a specific plant in ahorticultural field may be located. For example, according to missiondashboard 1100, mission m10080 requires locating a target identified byPUID pu_1231. Searching through PU lists 1200, AHF system 100 may findthat the target is located in local area A1 of field F04. A groundrobot, such as ground robot av03, may travel to local area A1 of fieldF04. After arriving at local area A1, ground robot av03 may scan some orall of the PU labels 1213, 1223, 1233, 1243, 1253, 1263, 1273 and 1283by maneuvering near PUs 1212, 1222, 1232, 1242, 1252, 1262, 1272 and1282 in a systematic way (e.g., moving from row to row, or moving fromthe edges of local area A1 spirally toward the middle of local area A1,etc.). Ground robot av03 may continue the maneuvering and the scanninguntil a PU label reveals PUID pu_1231. For instance, ground robot av03may start from the first row of the PUs and maneuver to a vicinity of PU1212 and scan PU label 1213, which reveals PUID pu_1211, different fromthe target PUID pu_1231. Ground robot av03 may subsequently maneuver toa vicinity of PU 1252 and scan PU label 1253, which reveals PUIDpu_1251, also different from the target PUID pu_1231. Ground robot av03may subsequently move to the second row of the PUs and maneuver to avicinity of PU 1282 and scan PU label 1283, which reveals PUID pu_1281,also different from the target PUID pu_1231. Ground robot av03 maysubsequently maneuver to a vicinity of PU 1232 and scan PU label 1233,which reveals PUID pu_1231, matching the target PUID. In this way,ground robot av03 is able to locate PU 1232, located in local area A1 offield F04, as the target of mission m10080.

As described above, for horticultural field F04, the administrativescheme of AHF system 100 may include the following set of administrativeitems: operation dashboard 300, local area map 400, physical structuremap 500, grow operation map 600, field activity map 700, RZ map 800,ground robot dashboard 1000, mission dashboard 1100, and PU lists 1200.Each of the administrative items may be updated constantly in areal-time or just-in-time manner. Since the administrative informationresiding in the administrative items is pertinent to field F04, it canbe advantageous to store the set of administrative items in a localserver that is physically located within a vicinity of field F04. Foreach horticultural field serviced by AHF system 100, the administrativescheme may include a similar set of administrative items (i.e., anoperation dashboard, a local area map, a physical structure map, a growoperation map, a field activity map, an RZ map, a ground robotdashboard, a mission dashboard, and a plurality of PU lists) pertinentto the respective horticultural field, and the set of administrativeitems may be stored in a local server of the horticultural field. Forexample, the administrative scheme of AHF system 100 may include a setof administrative items pertinent to field F01, and the set ofadministrative items may be saved in local server 121. Likewise, theadministrative scheme of AHF system 100 may also include a set ofadministrative items pertinent to greenhouse G02, and the set ofadministrative items may be saved in local server 122. Central server199 and master grower 198 may access, edit, and update theadministrative items for any horticultural field of AHF system 100 vianetwork 196 and user device 197. Additionally, field worker 191 mayaccess, edit, and update the administrative items of field F01 stored inlocal server 121 via personal communication device 193. Likewise, fieldworker 192 may access, edit, and update the administrative items ofgreenhouse G02 stored in local server 122 via personal communicationdevice 194.

In some embodiments, central server 199 may keep a synchronized copy ofthe administrative items of each horticultural field. This approach mayenable central server 199 to marshal administrative information acrossvarious horticultural fields, from which AHF system 100 may benefit. Forexample, ground robot dashboard 1000 indicates that ground robot av05,capable of lifting a heavy weight, is having a mechanical problem andthus not available for a horticultural mission. Central server 199 maythus command another ground robot capable of a high payload to move fromadjacent field F03 to filed F04 for a horticultural mission thatrequires a high payload ground robot.

FIG. 13 illustrates a block diagram of a computing server 1300, whichmay embody a local server (e.g., local server 121 or 122) or a centralserver (e.g., central server 199) of AHF system 100. As shown in FIG.13, computing server 1300 may include one or more processors 1310, acommunication hardware 1320, hardware 1330, and memory 1340.

Communication hardware 1320 may include a wired transceiver 1322 forwired communications, and a wireless transceiver 1326 for wirelesscommunications. Communication hardware 1320 may enable computing server1300 to communicate with other devices of AHF system 100, such as fieldsensors (e.g., sensors 111 and 112), robots (e.g., ground robots 131 and132, irrigation robot 161), horticultural devices (e.g., illuminationdevice 162), ground robot bays (e.g., vehicle bays 141 and 142),field-deployed visual devices (e.g., cameras 151 and 152), personalcommunication devices (e.g., personal communication devices 193 and194), and network 196. Various horticultural data and administrativeinformation may be transmitted and/or received through communicationhardware 1320.

Hardware 1330 may include other hardware that is typically located in acomputer or server. For example, hardware 1330 may include signalconverters, transceivers, antennas, hardware decoders and encoders,graphic processors, and/or the like that enable computing server 1300 toexecute applications or software programs, procedures, or algorithms.

Memory 1340 may be implemented using non-transitory computer-readablemedia, such as computer storage media. Computer-readable media includes,at least, two types of computer-readable media, namely, computer storagemedia and communications media. Computer storage media includes volatileand non-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. Computer storage media includes RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital optical disks orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other non-transmissionmedium that can be used to store information for access by a computingdevice. As defined herein, computer storage media do not consist of, andare not formed exclusively by, modulated data signals, such as a carrierwave. In contrast, communication media may embody computer-readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave, or other transmissionmechanism.

Memory 1340 may include programs or software procedures that, whenexecuted by processor(s) 1310, cause computing server 1300 to performvarious functions as described herein. As shown in FIG. 13, memory 1340may include an operating system 1341, an administrative scheme 1342, ahorticultural database 1343, an image analysis module 1344, aremediation module 1345, and a navigation module 1346. Operating system1341 may include components that manage or otherwise coordinateprocessor(s) 1310 and hardware 1330 with software resources to performvarious functions generally associated with a computer.

Administrative scheme 1342 may include various administrative itemsdescribed above. Administrative scheme 1342 may include operationdashboard 300, ground robot dashboard 1000, and mission dashboard 1100.Administrative scheme 1342 may also include local area map(s) 400,physical structure map(s) 500, grow operation map(s) 600, field activitymap(s) 700, RZ map(s) 800, and PU lists 1200. In an event that computingserver 1300 embodies a local server (e.g., local server 121 or 122),each of the administrative items of administrative scheme 1342 maycontain information regarding a particular field (e.g., field F01 orgreenhouse G02). In an event that computing server 1300 embodies acentral server (e.g., central server 199), each of the administrativeitems of administrative scheme 1342 may contain information from all thehorticultural fields serviced by AHF system 100. Processor(s) 1310 mayconstantly update the administrative items so that administrative scheme1342 may effectively reflect status of AHF system 100 effective in areal-time or just-in-time manner. Namely, each of the administrativeitems of administrative scheme 1342 may change from a moment to thenext. Moreover, new missions may be added to mission dashboard 1100 byremediation module 1345 or a human worker (e.g., master grower 195 or198 or field worker 191 or 192).

Horticultural database 1343 may store both plant-related andnon-plant-related horticultural data collected by sensors of AHF system100 via field-sensing and onboard-sensing approaches. A horticulturaldata entry may be recorded along with a time stamp and an identificationof one or more specific plants the horticultural data is pertinent to.In an event that computing server 1300 embodies a local server (e.g.,local server 121 or 122), horticultural database 1343 may storehorticultural data regarding a particular field (e.g., field F01 orgreenhouse G02). In an event that computing server 1300 embodies acentral server (e.g., central server 199), horticultural database 1343may store horticultural data collected from all the horticultural fieldsserviced by AHF system 100.

Image analysis module 1344 may include image processing algorithms orsoftware procedures that are able to process or otherwise render stillimages or video recordings stored in horticultural database 1343. Insome embodiments, the image processing algorithms may highlight orotherwise identify abnormal, unusual, or unique visual features thereofthat may be an indication of a horticultural problem. In someembodiments, the image processing algorithms may estimate a height, adensity of flower buds or fruits, a size or quantity of produces, etc.,based on the still images or video recordings stored in horticulturaldatabase 1343.

Remediation module 1345 may, based on raw horticultural data stored inhorticultural database 1343 or rendered image/video processed by imageanalysis module 1344, identify a horticultural problem. In someembodiments, remediation module 1345 may also prescribe or otherwisedetermine a remedial solution to address the horticultural problem. Insome embodiments, the remediation solution may trigger one or morehorticultural missions in AHF system 100.

Navigation module 1346 may direct or otherwise assist a ground robot tonavigate to a destination, where the ground robot may perform ahorticultural mission. It is to be noted that navigation module 1346 isnot intended to replace the onboard positioning/navigation function of aground robot. Rather, navigation module 1346 may work in concert withthe onboard positioning/navigation function to guide the ground robot tothe destination. For example, navigation module 1346 may determine,according to mission dashboard 1100, operation dashboard 300, and growoperation map 600, that ground robot av01 is required to travel to localareas A5-A7 and B5-B7 of field F04 for executing mission m10061. Usingwireless transceiver 1326 of communication hardware 1320, computingserver 1300 may transmit the destination information (i.e., “local areasA5-A7 and B5-B7 of field F04”), as determined by navigation module 1346,to ground robot av01 so that the onboard positioning/navigation functionof ground robot av01 may navigate ground robot av01 to the destination.

In some embodiments, navigation module 1346 may determine, in additionto a destination, a path along which a ground robot may arrive at thedestination. For example, firstly, navigation module 1346 may determine,according to mission dashboard 1100, operation dashboard 300, and growoperation map 600, that ground robot av04 is required to local areas A1,A2, B1 and B2 of field F04, where grow operation op512 is, for executingmission m10070. Secondly, navigation module 1346 may determine,according to ground robot dashboard 1000, that ground robot av04 isdocked in ground robot bay vb01, which is located in local area A7according to physical structure map 500. Thirdly, navigation module 1346may identify various RZs between local area A7 and the destination usingRZ map 800, and subsequently determine a path between local area A7 andlocal area B1 that avoids the RZs specified on RZ map 800. Inparticular, navigation module 1346 may determine path 910 between groundrobot bay vb01 and local area B1, whereas path 910 avoids all RZsspecified on RZ map 800, such as RZs 942, 943, and 944. Computing server1300 may transmit the destination information (i.e., “local areas A1,A2, B1 and B2 of field F04”), as well as path 910, to ground robot av04so that the onboard positioning/navigation function of ground robot av04may navigate ground robot av04 to the destination along path 910.

FIG. 14 and FIG. 15 present illustrative processes 1400 and 1500,respectively. Process 1400 provides a method for executing ahorticultural mission of an AHF process, whereas process 1500 provides amethod for dynamically updating a ground robot path as NRZs change. Eachof processes 1400 and 1500 is illustrated as a collection of blocks in alogical flow chart, which represents a sequence of operations that canbe implemented in hardware, software, or a combination thereof. In thecontext of software, the blocks represent computer-executableinstructions that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions mayinclude routines, programs, objects, components, data structures, andthe like that perform particular functions or implement particularabstract data types. The order in which the operations are described isnot intended to be construed as a limitation, and any number of thedescribed blocks can be combined in any order and/or in parallel toimplement the process. For discussion purposes, the processes 1400 and1500 are described with reference to FIGS. 1-13.

FIG. 14 is a flow diagram of an example process 1400 for executing ahorticultural mission of an AHF process. The horticultural mission mayinvolve performing a certain horticultural action to a target (e.g., aplant) located within a horticultural field. The horticultural missionmay be assigned to a ground robot that is physically away from thetarget, and the ground robot may travel to the target, while avoidingvarious RZs along the way, to perform the action. Depending on theessence of the mission, some results may be collected by the groundrobot, such as certain horticultural data pertinent to the target. Theground robot may transmit the horticultural data, either directly orindirectly, to a computing server in a real-time or just-in-time mannerfor further analysis. Process 1400 may include blocks 1410, 1420, 1430,1440, 1450, 1460, 1470, 1480, 1485 and 1490. Process 1400 may begin atblock 1410.

At block 1410, server 1300 may receive a horticultural mission. Themission may be entered into AHF system 100 by master grower 198. Themission may be listed in mission dashboard 1100, such as mission m10080therein. The horticultural mission may include an identification of atarget located within a horticultural field. The mission may alsoinclude an action to be performed with respect to the target. Forexample, as shown in mission dashboard 1100, mission m10080 includes atarget ID (i.e., pu_1231), as well as an action to be performed withrespect to the target (i.e., collect soil pH level).

In some embodiments, a mission may also include an intended time window,wherein the action is intended to be performed with respect to thetarget within the intended time window. For example, mission dashboard1100 records that mission m10080 has an intended time window between20:00 and 21:00 on May 22^(nd). That is, mission m10080 intends tocollect the soil pH level regarding a target having a target ID pu_1231between 20:00 and 21:00 on May 22^(nd). The intended time window isspecified in the mission for the sake of validity of the horticulturalprocess involving the mission. For example, the action of the missionmay involve collecting certain horticultural data with respect to thetarget, and the action has to be performed within a specific time frame(i.e., the intended time window) so that the horticultural data ascollected may be valid or meaningful for the subsequent analysis of thehorticultural data. Process 1400 may proceed from block 1410 to block1420.

In some embodiments, there may be a difference in time between thecompletion of block 1410 and the start of block 1420, notably in anevent that the mission includes an intended time window, as explainedbelow. Process 1400 aims to perform the action with respect to thetarget while the target remains stationary within the horticulturalfield. Due to horticultural activities, PUs of a horticultural field mayexperience frequent changes in locations within the horticultural field.This is not an uncommon scenario especially when the horticultural fieldis a greenhouse, as plants growing in a greenhouse may often need to bemoved around for horticultural and logistic purposes. In an event that amission includes an intended time window, a difference in time betweenthe completion of block 1410 and the start of block 1420 may berequired, so that the AHF system may perform the horticultural missionwhile the target is not having a location change. Specifically, server1300 may determine an immobile duration within the intended time window.During the immobile duration, the target PU is not scheduled to have alocation change. Moreover, server 1300 may wait until an onset of theimmobile duration arrives before proceeding to block 1420. This is toensure that the mission does not start to execute until the target isnot subject to a scheduled location change.

For example, mission dashboard 1100 indicates that mission m10080 isintended to be executed between 20:00 and 21:00 on May 22^(nd). Thetarget of mission m10080 is PU pu_1231. Based on the identification ofthe target, an immediate location of PU pu_1231 may be looked up in PUlists 1200, and thus determined as in local area A1 of field F04. Fieldactivity map 700 may indicate that PUs in local area A1 of field F04 arescheduled to relocate, on May 22^(nd), to local area A4 for six hoursand then back to local area A1, and the relocation is not scheduled tofinish until 20:30 on May 22^(nd). Server 1300 may thus determine thatan immobile duration for mission m10080 is 20:30-21:00 on May 22^(nd).

At block 1420, server 1300 may determine, based on the identification ofthe target, a local area of the horticultural field, wherein the targetis located within the local area. For example, by looking up target IDpu_1231 in PU lists 1200, server 1300 may determine that the target islocated within local area A1 of field F04. Process 1400 may proceed fromblock 1420 to block 1430.

At block 1430, server 1300 may identify one or more RZs within thehorticultural field. For example, server 1300 may access RZ map 800,which identifies a plurality of RZs within field F04. Process 1400 mayproceed from block 1430 to block 1440.

At block 1440, server 1300 may assign the mission to at least one of aplurality of ground robots servicing the horticultural field. Forexample, as recorded in mission dashboard 1100, server 1300 may assignmission m10080 to ground robot av03. Namely, the identification of thetarget (i.e., PUID pu_1231) and the action (i.e., collect a pH levelmeasurement reading of the soil) are both made known to ground robotav03.

In some embodiments, block 1440 may be implemented in several sequentialsteps. Firstly, block 1440 may involve server 1300 determining aquantity of ground robots needed for performing the mission. For mosthorticultural missions, such as mission m10080, a single ground robotmay be enough. However, depending on the action to be performed in amission, two or more ground robots may be required. For example, amission may intend to cover a local area with a shade screen of arectangle shape. The mission would be extremely difficult to execute ifusing only one ground robot. Server 1300 may determine that four groundrobots are needed to execute the mission, with one ground robot carryinga respective corner of the shade screen.

Secondly, block 1440 may involve server 1300 determining resourcesneeded for the mission. For example, server 1300 may determine that, inorder to carry the shade screen, each of the four ground robots needs tohave a payload of at least 5 lbs.

Thirdly, block 1440 may involve server 1300 performing a resource checkon ground robots until a number of ground robots equal to or exceedingthe quantity of ground robots needed pass the resource check. Forexample, server 1300 may check the ground robots listed in ground robotdashboard 1000 until four ground robots each having a payload of 5 lbsor more are identified. Assuming all ground robots listed in groundrobot dashboard 1000 are available for the moment, server 1300 mayperform a resource check by checking the payload specification of theground robots. Server 1300 may subsequently determine that ground robotsav01, av05, av06 and av07 pass the resource check, as each of the fourground robots has a payload that is at least 5 lbs. When performing theresource check, server 1300 may preferably check the ground robots thatare closer to the target. Specifically, server 1300 may begin with aground robot that is located closest to the target, and then continuewith other ground robots based on a distance between the respectiveground robot and the target in an ascending order. Namely, server 1300may start from a ground robot that is located the closest to the targetto see if the specific ground robot passes the resource check. Server1300 may then perform the resource check on a ground robot that issecond closest to the target. Server 1300 may then continue the resourcecheck with the third closest ground robot from the target, the fourthclosest ground robot, the fifth closest ground robot, and so on, untilground robots of the needed quantity have passed the resource check.This approach may ensure the ground robots executing the mission arelocated relatively close to the target of the mission.

Fourthly, block 1440 may involve server 1300 assigning the mission tothe number of ground robots that pass the resource check. For example,server 1300 may assign the mission of cover the local area with theshade screen to ground robots av01, av05, av06 and av07. Process 1400may proceed from block 1440 to block 1450.

At block 1450, server 1300 may determine, for the ground robot (or eachof the ground robots) to which the mission is assigned, a path alongwhich the ground robot may travel to the target. Specifically, the pathis required to avoid RZs as specified in RZ map 800. As an example, path910 is determined for ground robot 920 to move along, whereas path 910avoids RZs 941-946. Process 1400 may proceed from block 1450 to block1460.

At block 1460, server 1300 may maneuver the ground robot (or each of theground robots) along the path determined at block 1450 to the local areadetermined at block 1420. Specifically, navigation module 1346 of server1300 may notify the ground robot (or each of the ground robots) aboutthe destination (i.e., the local area) determined at block 1420 as wellas the path determined at block 1450. The ground robot (or each of theground robots) may maneuver itself along the path to the destinationusing the onboard positioning/navigation functions or an externalpositioning mechanism such as positioning mechanism 200. For example,using positioning mechanism 200, ground robot av03 may follow the pathdetermined at block 1420 to arrive at local area A1 of field F04. Asdescribed above, the ground robot may travel along the path within acertain proximity (e.g., ground robot 920 traveling along path 910within a range of proximity 911). In some embodiments, the ground robotcan move to follow the path within predetermined tolerances or bounds,for example diverting no more than a predetermined or assigned distancefrom a center of the path (which can be defined as a line), such as plusor minus three feet laterally or vertically. The path can also bedefined as a continuous airspace region through which the ground robotis authorized to travel, and parameters can be set so that the groundrobot travels through the region or path while maintaining predetermineddistances from lateral boundaries of the region. Limits can be definedproportionally (e.g., staying within a central third of any confiningdimension of the path) and/or discretely (e.g., no closer than threefeet to any path boundary), and can appropriately vary along the pathaccording to various conditions such as obstacles, prevailing winds, orother hazards. Process 1400 may proceed from block 1460 to block 1470after the ground robot (or each of the ground robots) arrives at thedestination.

At block 1470, the ground robot (or each of the ground robots) maylocate the target within the local area. For example, in executingmission m10080, ground robot av03 may, after arriving at local area A1of field F04, locate the target within local area A1 by scanning one ormore of PU labels 1213, 1223, 1233, 1243, 1253, 1263, 1273 and 1283.Specifically, upon scanning PU label 1233, ground robot av03 mayrecognize that PU label 1233 reveals PUID pu_1231, which matches theidentification of the target of mission m10080. Therefore, ground robotav03 may locate the plant growing in PU 1232 to be the target of missionm10080. Process 1400 may proceed from block 1470 to block 1480.

At block 1480, the ground robot(s) may perform the action with respectto the target. Some missions may not involve an action of collectinghorticultural data pertinent to the target, whereas some other missionsmay. For example, ground robot av03 may collect a pH level reading ofthe soil of PU 1232 by receiving the pH level reading from a pH meterembedded in the soil of PU 1232. ground robot av03 may receive the pHlevel reading using a low-power/short-range wireless communicationtechnology while maneuvering near PU 1232. ground robot av03 maytemporarily store the pH level reading in an onboard memory of groundrobot av03. Process 1400 may proceed from block 1480 to block 1485.

At block 1485, server 1300 may determine the status of the groundrobot(s) based on whether the action involves collecting horticulturaldata. In an event that the action does not involve collectinghorticultural data, process 1400 may proceed from block 1485 to 1410.That is, the ground robot(s) have completed the mission and are ready tobe assigned a new mission. In an event that the action involvescollecting horticultural data, process 1400 may proceed from block 1485to 1490.

At block 1490, a ground robot may, after performing the action of themission, transmit the horticultural data as collected to a computingserver for further analysis. For example, as part of the execution ofmission m10080, ground robot av03 may transmit the soil pH level readingpertinent to PU 1232, as collected, to a local area server of field F04for analysis. In some embodiments, server 1300 may maneuver the groundrobot to a data transfer bay (i.e., a vehicle bay that serves as a datatransfer station), where the ground robot may transfer the horticulturaldata as collected to a storage device located at the data transfer bay.The horticultural data may be transmitted from the storage device at thedata transfer bay to a computing server for analysis. For example, inexecuting a horticultural mission, a ground robot 132 may take stillimages of grow operation 103 of greenhouse G02, and save the stillimages in an onboard memory of the ground robot 132. The ground robot132 may then travel to vehicle bay 142, which may be a data transferbay. The ground robot 132 may then transfer the still images of growoperation 103 from the onboard memory to a storage device of vehicle bay142. Subsequently, the still images of grow operation 103 stored at thestorage device of vehicle bay 142 may be uploaded to local server 122and saved in horticultural database 1343 of local server 122 using wiredor wireless communication techniques. The still images of grow operation103 may then be processed and analyzed by image analysis module 1344 andremediation module 1345 to identify possible horticultural issues ofgrow operation 103. Process 1400 may proceed from block 1490 to 1410.

In some embodiments, blocks 1480, 1485 and 1490 may not necessarilyoccur in sequence. Instead, blocks 1480, 1485 and 1490 may in somerespects occur concurrently, either in parallel or in an overlappingfashion. For example, a horticultural mission assigned to a ground robot131 may involve an action of taking live video recording of growoperation 102 as irrigation robot 161 moves along and irrigates growoperation 102. The ground robot 131 may, while in the process ofrecording, wirelessly transmit the recorded video footage to a storagedevice of a vehicle bay 141 in a real-time manner. Master grower 198 mayreal-time monitor the irrigation process shown on user device 197 byaccessing the video footage stored in the storage device via network 196and the communication link between local server 121 and the vehicle bay141. The video footage may be transmitted to central server 199 forfurther analysis or storing a copy. In an event that the ground robot131 cannot establish a direct wireless communication link to the vehiclebay 141 (e.g., the ground robot 131 being too far away from the vehiclebay 141 and thus out of a communication range), one or more other groundrobots 131 may be deployed to establish an airborne communication link,via which the video footage may be passed from one ground robot 131 tothe next ground robot 131 and eventually to the storage device of thevehicle bay 141.

In some embodiments, a ground robot may execute multiple missions beforethe ground robot transmits the collected horticultural data. This isparticularly the case if AHF system 100 does not need the horticulturaldata immediately or soon. Namely, horticultural data collected fromseveral missions may be all be temporarily stored in an onboard memoryof the ground robot, and then be transmitted to a computing server foranalysis at a later time.

FIG. 15 is a flow diagram of an example process 1500 for dynamicallyupdating a ground robot path as RZs changes. Process 1500 may be appliedor otherwise combined with process 1400 to enhance the respectiveprocess by providing resilience of a ground robot path in the face of RZchanges. Process 1500 may include blocks 1510, 1520, 1530, 1540 and1550. Process 1500 may begin at block 1510.

At block 1510, server 1300 may save information regarding RZs specifiedon RZ map 800 into a corresponding RZ list. RZ map 800 and thecorresponding RZ list contain essentially the same information, i.e.,where the RZs are defined in a horticultural field at the moment.Process 1500 may proceed from block 1510 to block 1520.

At block 1520, server 1300 may compare a current version of the RZ listwith an immediately previous version of the RZ list to find anyincremental change in the RZs specified thereon. In an event that server1300 finds no change in RZs between the two versions, process 1500 maystay at block 1520. In an event that a change in RZs is found, process1500 may proceed from block 1520 to block 1530.

At block 1530, server 1300 may update the RZ list according to the mostrecent RZ map 800 to reflect the change(s) in RZs. Process 1500 mayproceed from block 1530 to block 1540.

At block 1540, server 1300 may transmit the updated RZ list to groundrobots that are currently deployed for missions. Namely, each groundrobot that is currently deployed for a mission may receive a copy of themost recent RZ list. Process 1500 may proceed from block 1540 to block1550.

At block 1550, each airborne ground robot may check whether the paththat it is currently traveling along may interfere with the RZsspecified in the most recent RZ list. In an event that the current pathmay interfere with an RZ therein, the positioning/navigation functionsof the ground robot may update the path to avoid all RZs specified inthe most recent RZ list. Alternatively, navigation module 1346 of server1300 may update the path based on the most recent RZ list and send theupdated path to the ground robot to follow along.

FIG. 16 is a system diagram showing aspects of one illustrative systemdisclosed herein for servicing horticultural fields using ground robots.As shown in FIG. 16, a system 1600 may include a remote computer 1601,an autonomous device 1602, and a network 1620. For illustrativepurposes, the autonomous device 1602 is also referred to herein as a“autonomous vehicle 1602” “ground robot 1602” or a “robot 1602” or a“second computing device 1602.” It should be understood that some or allof the functions and components associated with autonomous device 1602and remote computer 1601 may be implemented on a single device ormultiple devices.

The remote computer 1601 and the robot 1602 may be interconnectedthrough one or more local and/or wide area networks, such as the network1620. In addition, the robot 1602 may be in communication with theremote computer 1601 and other computers by the use of one or morecomponents. For instance, the robot 1602 may be equipped with one ormore light sources, and the remote computer 1601 may include one or moresensors, including a camera, for detecting the location of the robot1602. As will be described in more detail below, the robot 1602 may beconfigured with light sources, sensors and transmitting devices tofacilitate communication with one or more devices. Other wired orwireless communication mechanisms may be utilized to providecommunication between one or more components and/or devices shown inFIG. 16 and other components or computers. In some configurations, therobot 1602 can also include an input device, a sensor, such as a camera,or other devices for generating image data or input data 1613. Any dataobtained or generated by the robot 1602 can be communicated to anothercomputer or device, such as the remote computer 1601. It should beappreciated that many more network connections may be utilized thanillustrated in FIG. 16.

The remote computer 1601 may be in the form of a personal computer, aserver, a laptop, or any other computing device having components forcausing a display of one or more images on a display, such as aninterface 1648. In one illustrative example, the interface 1648 mayinclude a screen configured to provide a graphical user interface.

The remote computer 1601 may comprise a sensor 1653, such as a sonarsensor, a depth sensor, infrared sensor, heat sensor, touch sensor, orany other device or component for detecting the presence, position,and/or characteristics of an object. In addition, the remote computer1601 can comprise an input device 1619, such as a keyboard, mouse,microphone, or any other device configured to generate a signal and/ordata based on any interaction with the remote computer 1601. Forillustrative purposes, signals or data provided by a component, such asthe sensor 1653 or the input device 1619 is referred to herein as inputdata 1613. Input data 1613 may also include contextual data or otherdata received from a computing system, such as the remote computer 1601,or a server providing a resource or service.

The robot 1602 may include a local memory 1680 that stores profile data1603, input data 1613, and application data 1645. The profile data 1603may store information describing user activity, preferences and otherinformation used for providing control of robot 1602. The applicationdata 1645 may include output data generated by techniques disclosedherein.

The robot 1602 may also include a program module 1611 configured tomanage techniques described herein and interactions between a robot andthe remote computer 1601. For example, the program module 1611 may beconfigured with one or more surface reconstruction algorithms and otheralgorithms for locating objects and devices. The surface reconstructionalgorithms and other algorithms may use data or signals collected fromone or more sensors 1653, such as a depth sensor attached to the robot1602.

The robot 1602 may be equipped with a control module 1650 for executinginstructions communicated to the robot 1602. The robot 1602 may have oneor more control components, such as an actuator 1652. Components of therobot 1602, such as the actuator 1652, may be configured to generate aphysical movement of one or more objects from instructions received bythe robot 1602. Robot 1602 may also comprise a number of motorsconfigured to control the movement of the robot 1602.

In some aspects of the disclosure, the robot 1602 detects one or moreconditions based on the input data 1613 and other data and generates oneor more instructions for controlling the robot 1602. In someconfigurations, the robot 1602 obtains input data 1613 and other datadescribing the location and status of the robot 1602. In addition, therobot 1602 may obtain and process data indicating a location of therobot 1602 relative to the remote computer 1601.

Any input data 1613 received from any resource, such as a remotecomputer or a sensor, may be used by the robot 1602 to determine thelocation of any object, the location of the remote computer 1601 and thelocation of the robot 1602. For instance, the robot 1602 may include oneor more sensors for obtaining depth map data, such as a depth sensor,and other data to identify the location of various objects in a room,including the room boundaries. Configurations disclosed herein cangenerate data describing geometric parameters of any object or boundary.

Any known technology for identifying the location of one or more objectsmay be used by the techniques disclosed herein. In one example, datadefining the location of the robot 1602 or a person may be obtained bythe use of an optical sensor, such as a camera or any other sensor 1653or input device 1619, and lights or other visual elements mounted on therobot 1602.

These examples are provided for illustrative purposes only and are notto be construed as limiting. Any technology may be used for identifyinga location of any computing device or object, which may involve the useof a radio signal, a light-based signal or any signal capable ofidentifying the location of an object. The robot 1602 may process anyinput data 1613 from any device or resource to identify the location andother contextual information regarding objects or computing devices.

In some configurations, the robot 1602 may have one or more sensors forcapturing and generating data. In one illustrative example, the robot1602 may be equipped with one or more depth map cameras. The depth mapcameras, or any other type of sensor, may collect data describingobjects detected by the sensors. In yet another example, the robot 1602may be equipped with a wheel position sensor. Data or a signal generatedby such sensors, such as the wheel position sensor, may be used toidentify the location, velocity or other information regarding the robot1602. These examples are provided for illustrative purposes only and arenot to be construed as limiting. It can be appreciated that a number ofsensors or devices may be used to generate/obtain data associated withone or more objects and to identify the location of one or more objects.

The obtained data, such as depth map data, may be then processed toidentify objects and the location of objects, and to generate anddisplay data associated with the object. In some embodiments, the dataassociated with the object may be displayed on a user interface with arepresentation or graphical element that shows an association betweenthe data associated with the object and an object. For illustrativepurposes, data that is associated with an object is referred to hereinas “attached data” or data that is “attached” to an object. In addition,any obtained data, also referred to herein as input data 1613, may beused for generating and modifying instructions for the robot 1602. Insome configurations, robot 1602 can be configured to perform or managecomplex navigation and pathfinding tasks.

In some configurations, the robot 1602 interprets input data 1613 and/orother data to determine a context with respect to objects in itsvicinity. The robot 1602 may perform one or more functions, such as adepth map analysis and surface reconstruction analysis to identifyobjects and properties of objects. For instance, certain geometricshapes and other parameters, such as a size of an object, may be used tocategorize or characterize individual objects, e.g., an object may becharacterized as “fence,” a “high-priority object,” or a “primaryobject.” Other data related to objects in an environment may be obtainedfrom databases or other resources.

In some configurations, the robot 1602 may process input data 1613 fromone or more resources to generate contextual data. The contextual datacan be used to identify a location associated with each identifiedobject. Based on location information, other data, and other propertiesassociated with each object, the robot 1602 can generate instructions toperform one or more tasks. The generated instructions may be based onthe location of the identified objects, such as a computer, geometricdata, characteristics of an object, and other contextual information.

To illustrate aspects of the techniques disclosed herein, consider ascenario where the robot 1602 is in an environment, e.g., a field, withother objects. Sensors 1653 and input devices 1619 can generate signalsor data associated with the objects. For instance, the signals or datacan be processed by one or more methods, such as technologies involvingtriangulation algorithms, to identify the location of the objects and/orthe robot 1602. Other input data 1613 may be received and processed withthe signals or data to identify the location of the objects and/or therobot 1602 and other parameters, such as the size and shape of theobjects and/or the robot 1602. Processing can be applied to any receiveddata or signal to identify the location and geometric properties ofobjects in the vicinity. The obtained information can be used togenerate one or more instructions that may be processed by the robot1602 for execution. The instructions enable the robot 1602 to performone or more tasks, which may involve interaction between the robot 1602and one or more objects in the room.

The ability for a horticultural feedback system to employ robots,including ground robots, to aid in horticultural feedback processesprovides tremendous benefits in terms of cost, efficiency, andeffectiveness, as compared to traditional horticultural feedback systemsthat heavily rely on human labor. A coherent administrative schemeproviding real-time or just-in-time marshaling of information regardingAHF activities is crucial to the performance of an AHF system. Thanks tothe administrative scheme, horticultural missions may be optimallyassigned to, and executed by, ground robots equipped with variouscameras, sensors, and other resources. Furthermore, RZs may becomprehensively identified and updated based at least on types of growoperations, physical structures in the field, horticultural activitiesbeing conducted, weather, technical specifications of ground robots,which contributes to safe and efficient operation of ground robots.

The disclosure presented herein encompasses the subject matter set forthin the following example clauses.

Example 1: A method for servicing a horticultural operation comprisingone or more local areas, the method comprising:

receiving, by a computing system, data from one or more autonomousvehicles, the data pertaining to the horticultural operation or one ormore targets located within the horticultural operation;

analyzing the received data to determine one or more conditions of theone or more targets;

based on the analyzing, determining one or more recommendations foraddressing the one or more conditions;

sending the determined conditions and recommendations to a userinterface;

when authorized, transmitting data to the one or more autonomousvehicles that are indicative of follow-on actions for the target; and

receiving additional data, when available, based on the follow-onactions for further analysis.

Example 2: The method of example 1, wherein the analyzing comprisesstitching together a set of images, isolating one or more local areasfrom the stitched image, and analyzing the isolated local areas

Example 3: The method of example 1, further comprising receiving sensordata from one or more sensors configured to capture data pertaining tothe target.

Example 4: The method of example 3, wherein the one or more sensorsinclude environmental sensors or image capturing devices, wherein theenvironmental sensors including at least one of range-finding sensors,light intensity sensors, light spectrum sensors, non-contact infra-redtemperature sensors, thermal sensors, photoelectric sensors that detectchanges in color, carbon dioxide uptake sensors, water, pH testing, andoxygen production sensors, and wherein the image capturing devicescomprise RGB, hyperspectral, thermal, or LIDAR imaging devices.

Example 5: The method of example 3, wherein the sensors are coupled tonon-vehicles to augment the captured data.

Example 6: The method of example 1, wherein the local area comprises aplurality of plant units, wherein the target is a plant unit or a groupof plant units.

Example 7: The method of example 1, wherein the data comprises one of:

a composite image of the target or an area surrounding the target;

an image of the target;

an estimated height of the target;

a 3D surface mesh analysis of the target;

estimated volume of the target;

a temperature reading in a vicinity of the target;

a humidity reading in a vicinity of the target;

an illumination reading in a vicinity of the target;

a pH level of soil or water in which the target is planted;

a physical sample of the target;

a germination state of the target;

canopy coverage of the target;

canopy growth of the target;

flower/bud count of the target;

disease or anomaly regions of the target;

estimated vapor pressure deficit (VPD) of leaves of the target;

estimated temperature of leaves of the target; or flower/bud density ofthe target.

Example 8: The method of example 1, wherein the determining one or morerecommendations is performed by a machine learning component.

Example 9: The method of example 1, wherein the one or morerecommendations include at least one of changing a light intensity or alight spectrum of lighting, changing an amount of water or a frequencyof a watering operation, changing an amount of nutrients or fertilizer,changing a ratio of nutrients to fertilizer, changing an airflow,changing a temperature, changing an airflow intensity, changing anairflow direction schedule, or changing an automated spraying ofpesticides.

Example 10: The method of example 1, further comprising determining aprogress metric of the target, the progress metric indicative ofprogress of the target relative to predetermined milestones.

Example 11: The method of example 10, wherein the analyzing comprisesdetermining that the progress metric is not meeting the predeterminedmilestones; wherein the one or more recommendations comprise generatingone or more actions to improve the progress.

Example 12: The method of example 1, wherein the follow-on actionsinclude actions for automation of at least one plant grower action forthe target.

Example 13: A system, comprising:

one or more processors;

memory having instructions stored therein, wherein the instructions,when executed by the one or more processors, cause the system to:

receive data from one or more autonomous vehicles, the data pertainingto a horticultural operation or one or more targets located within thehorticultural operation;

analyze the received data to determine one or more conditions of the oneor more targets;

based on the analyzing, determine one or more recommendations foraddressing the one or more conditions;

send the determined conditions and recommendations to a user interface;

when authorized via the user interface, transmit data to the one or moreautonomous vehicles that are indicative of follow-on actions for thetarget; and

receive additional data, when available, based on the follow-on actionsfor further analysis.

Example 14: The system of example 13, further comprising instructionsstored therein, wherein the instructions, when executed by the one ormore processors, cause the one or more processors to receive sensor datafrom one or more sensors configured to capture data pertaining to thetarget.

Example 15: The system of example 13, wherein the data comprises one of:

a composite image of the target or an area surrounding the target;

an image of the target;

an estimated height of the target;

a 3D surface mesh analysis of the target;

estimated volume of the target;

a temperature reading in a vicinity of the target;

a humidity reading in a vicinity of the target;

an illumination reading in a vicinity of the target;

a pH level of soil or water in which the target is planted;

a physical sample of the target;

a germination state of the target;

canopy coverage of the target;

canopy growth of the target;

flower/bud count of the target;

disease or anomaly regions of the target;

estimated vapor pressure deficit (VPD) of leaves of the target;

estimated temperature of leaves of the target; or

flower/bud density of the target.

Example 16: The system of example 13, wherein the determine one or morerecommendations is performed by a machine learning component.

Example 17: A computer-readable medium comprising instructions storedtherein, wherein the instructions, when executed by a system comprisingone or more processors, cause the system to:

receive data from one or more autonomous vehicles, the data pertainingto a horticultural field or one or more targets located within thehorticultural field;

analyze the received data to determine one or more conditions of the oneor more targets;

based on the analyzing, determine one or more recommendations foraddressing the one or more conditions;

send the determined conditions and recommendations to a user interface;

transmit data to the one or more autonomous vehicles that are indicativeof follow-on actions for the target; and

receive additional data, when available, based on the follow-on actionsfor further analysis.

Example 18: The computer-readable medium of example 17, wherein the oneor more recommendations include at least one of changing a lightintensity or a light spectrum of lighting, changing an amount of wateror a frequency of a watering operation, changing an amount of nutrientsor fertilizer, changing a ratio of nutrients to fertilizer, changing anairflow, changing a temperature, changing an airflow intensity, changingan airflow direction schedule, or changing an automated spraying ofpesticides.

Example 19: The computer-readable medium of example 17, furthercomprising determining a progress metric of the target, the progressmetric indicative of progress of the target relative to predeterminedmilestones; wherein the analyzing comprises determining that theprogress metric is not meeting the predetermined milestones; wherein theone or more recommendations comprise generating one or more actions toimprove the progress.

Example 20: The computer-readable medium of example 16, wherein thefollow-on actions include actions for automation of at least one plantgrower action for the target.

The disclosure presented herein encompasses the subject matter set forthin the following example clauses.

Example 1: A method implementable to a horticultural operationcomprising one or more local areas, the method implemented by a systemconfigured to autonomously interact with the horticultural operation,the method comprising:

autonomously identifying the horticultural operation or a target locatedwithin the horticultural operation and an action to be performed withrespect to the horticultural operation or target, the operation ortarget comprising at least one plant or a group of plants;

determining, based on the identifying, a local area of the horticulturaloperation, wherein the target is located within the local area;

associating the horticultural operation or target to at least oneautonomous vehicle;

locating, by the at least one autonomous vehicle, the horticulturaloperation or target within the local area; and

performing the action with respect to the horticultural operation ortarget by the at least one autonomous vehicle.

Example 2: The method of example 1, further comprising determining apath between the at least one autonomous vehicle and the local area, thepath avoiding one or more restricted zones; wherein each of the one ormore restricted zones is continuous and comprises at least a portion ofthe local area.

Example 3: The method of example 2, further comprising using 3D data tomap obstacles.

Example 4: The method of example 2, wherein the one or more restrictedzones comprises one of the following:

a portion of the local area above a first altitude or below a secondaltitude;

a portion of the local area at a ground level; or

an identified object within the local area.

Example 5: The method of example 2, wherein the one or more restrictedzones are determined based on one or more of the following:

a custom master grower defined region;

a height of a growing plant within a local area;

a location of a physical structure;

a local area currently having a horticultural activity;

a passage reserved for ground traffic;

an air corridor reserved for aerial traffic;

an area in which flying is restricted by a regulation; or

a weather condition.

Example 6: The method of example 1, further comprising:

determining a quantity of autonomous vehicles needed for performing amission;

performing a resource check for autonomous vehicles until a number ofautonomous vehicles equal to or exceeding the quantity pass the resourcecheck; and

assigning the mission to the number of autonomous vehicles.

Example 7: The method of example 1, further comprising:

determining a set of sub-tasks and a corresponding quantity ofautonomous vehicles needed for performing a mission;

performing a resource check on available autonomous vehicles;

dynamically assigning a sub-task to available autonomous vehicles thatpass a resource check; and

continuing to assign sub-tasks as autonomous vehicles become availableuntil all sub-tasks of the set of sub-tasks have been completed.

Example 8: The method of example 6, wherein the performing of theresource check is performed in parallel or begins with one of theautonomous vehicles that is located closest to the target and continueswith others of the autonomous vehicles based on a distance between therespective autonomous vehicle and the target.

Example 9: The method of example 1, wherein:

the at least one autonomous vehicle is maneuvered based on one or moreof GPS, GLONASS, RTK, inertial navigation, or visual odometry.

Example 10: The method of example 1, wherein:

a plurality of beacons is disposed within the horticultural field; and

the beacons are located at defined 3D positions and emits a respectivesignal, the signal comprising a self-identifying RF signal, temporal orspatial visual patterns that can be captured by a camera, and whereinthe signal is usable by an autonomous vehicle to determine a location ora path.

Example 11: The method of example 1, wherein QR identification devicesare disposed in known positions within the horticultural field, and theautonomous vehicles are configured to compute a position based on knownpositions of the QR identification devices.

Example 12: The method of example 1, wherein the local area comprises aplurality of plant units, wherein the target is a plant unit or a groupof plant units, wherein each of the plant units or group of plant unitsis associated with a machine-readable code, and wherein the locating ofthe target within the local area comprises scanning the machine-readablecode of the plant unit or group of plant units.

Example 13: The method of example 12, wherein the machine-readable codeis used to define one or more boundaries of the local area and identifythe local area.

Example 14: The method of example 1, wherein the action comprisescollecting horticultural data pertinent to the target, the methodfurther comprising one or more of:

transmitting the horticultural data to a system for analysis after theaction is performed; or

performing the analysis on the autonomous vehicle.

Example 15: The method of example 1, wherein the action comprisescollecting horticultural data pertinent to the target, the methodfurther comprising one or more of:

maneuvering the at least one autonomous vehicle to a data transfer bayafter the action is performed and transferring the horticultural datacollected by the at least one autonomous vehicle to a storage devicelocated at the data transfer bay; or

wirelessly transmitting the horticultural data to a data transferstation, a Wifi network, or to a cell tower.

Example 16: The method of example 15, wherein the horticultural datacomprises one of the following:

a composite image of the target or an area surrounding the target;

an image of the target;

an estimated height of the target;

a mesh analysis of the target;

estimated volume of the target;

a temperature reading in a vicinity of the target;

a humidity reading in a vicinity of the target;

an illumination reading in a vicinity of the target;

a pH level of soil or water in which the target is planted;

a physical sample of the target;

a germination state of the target;

canopy coverage of the target;

canopy growth of the target;

flower/bud count of the target; or

disease or anomaly regions of the target.

Example 17: The method of example 15, further comprising determiningthat the target and only the target is in the horticultural data.

Example 18: A system, comprising:

a vehicle bay hosting a plurality of autonomous vehicles

one or more processors;

memory having instructions stored therein, wherein the instructions,when executed by the one or more processors, cause the one or moreprocessors to:

identify horticultural operation or a target located within thehorticultural operation and an action to be performed with respect tothe horticultural operation or the target, the operation or targetcomprising at least one plant or a group of plants;

determine, based on the identification, a local area of the operation ortarget;

assign the target to at least one autonomous vehicle of the plurality ofautonomous vehicles;

locate, by the at least one autonomous vehicle based on theidentification, the local area; and

perform the action with respect to the operation or target by the atleast one autonomous vehicle.

Example 19: The system of example 18, further comprising a plurality ofcameras disposed across the horticultural field, each of the pluralityof cameras capable of monitoring one or more of the local areas andproviding image data to the autonomous vehicles or the system.

Example 20: An autonomous vehicle configured to:

interact with a horticultural operation;

identify the horticultural operation or a target located within thehorticultural operation and an action to be performed with respect tothe operation or target;

locate, based on the identification, the operation or target; and

perform the action with respect to the target;

wherein the action comprises capturing data usable to autonomouslyanalyze conditions for one or more plants within the horticulturaloperation.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as example forms ofimplementing the claims.

What is claimed is:
 1. A method for servicing a horticultural operationcomprising one or more local areas, the method comprising: receiving, bya computing system, data from one or more autonomous vehicles, the datapertaining to the horticultural operation or one or more targets locatedwithin the horticultural operation; analyzing the received data todetermine one or more conditions of the horticultural operation or oneor more targets; based on the analyzing, determining one or morerecommendations for addressing the one or more conditions; sending thedetermined conditions and recommendations to a user interface; whenauthorized, transmitting data to the one or more autonomous vehiclesthat are indicative of follow-on actions for the horticultural operationor target; and receiving additional data, when available, based on thefollow-on actions for further analysis.
 2. The method of claim 1,wherein the analyzing comprises stitching together a set of images,isolating one or more local areas from the stitched image, and analyzingthe isolated local areas.
 3. The method of claim 1, further comprisingreceiving sensor data from one or more sensors configured to capturedata pertaining to the target.
 4. The method of claim 3, wherein the oneor more sensors include environmental sensors or image capturingdevices, wherein the environmental sensors including at least one ofrange-finding sensors, light intensity sensors, light spectrum sensors,non-contact infra-red temperature sensors, thermal sensors,photoelectric sensors that detect changes in color, carbon dioxideuptake sensors, water, pH testing, and oxygen production sensors, andwherein the image capturing devices comprise RGB, hyperspectral,thermal, or LIDAR imaging devices.
 5. The method of claim 3, wherein thesensors are coupled to non-vehicles to augment the captured data.
 6. Themethod of claim 1, wherein the local area comprises a plurality of plantunits, wherein the target is a plant unit or a group of plant units. 7.The method of claim 1, wherein the data comprises one of: a compositeimage of the target or an area surrounding the target; an image of thetarget; an estimated height of the target; a 3D surface mesh analysis ofthe target; estimated volume of the target; a temperature reading in avicinity of the target; a humidity reading in a vicinity of the target;an illumination reading in a vicinity of the target; a pH level of soilor water in which the target is planted; a physical sample of thetarget; a germination state of the target; canopy coverage of thetarget; canopy growth of the target; flower/bud count of the target;disease or anomaly regions of the target; estimated vapor pressuredeficit (VPD) of leaves of the target; estimated temperature of leavesof the target; or flower/bud density of the target.
 8. The method ofclaim 1, wherein the determining one or more recommendations isperformed by a machine learning component.
 9. The method of claim 1,wherein the one or more recommendations include at least one of changinga light intensity or a light spectrum of lighting, changing an amount ofwater or a frequency of a watering operation, changing an amount ofnutrients or fertilizer, changing a ratio of nutrients to fertilizer,changing an airflow, changing a temperature, changing an airflowintensity, changing an airflow direction schedule, or changing anautomated spraying of pesticides.
 10. The method of claim 1, furthercomprising determining a progress metric of the target, the progressmetric indicative of progress of the target relative to predeterminedmilestones.
 11. The method of claim 10, wherein the analyzing comprisesdetermining that the progress metric is not meeting the predeterminedmilestones; wherein the one or more recommendations comprise generatingone or more actions to improve the progress.
 12. The method of claim 1,wherein the follow-on actions include actions for automation of at leastone plant grower action for the target.
 13. A system, comprising: one ormore processors; memory having instructions stored therein, wherein theinstructions, when executed by the one or more processors, cause thesystem to: receive data from one or more autonomous vehicles, the datapertaining to a horticultural operation or one or more targets locatedwithin the horticultural operation; analyze the received data todetermine one or more conditions of the one or more targets; based onthe analyzing, determine one or more recommendations for addressing theone or more conditions; send the determined conditions andrecommendations to a user interface; when authorized via the userinterface, transmit data to the one or more autonomous vehicles that areindicative of follow-on actions for the target; and receive additionaldata, when available, based on the follow-on actions for furtheranalysis.
 14. The system of claim 13, further comprising instructionsstored therein, wherein the instructions, when executed by the one ormore processors, cause the one or more processors to receive sensor datafrom one or more sensors configured to capture data pertaining to thetarget.
 15. The system of claim 13, wherein the data comprises one of: acomposite image of the target or an area surrounding the target; animage of the target; an estimated height of the target; a 3D surfacemesh analysis of the target; estimated volume of the target; atemperature reading in a vicinity of the target; a humidity reading in avicinity of the target; an illumination reading in a vicinity of thetarget; a pH level of soil or water in which the target is planted; aphysical sample of the target; a germination state of the target; canopycoverage of the target; canopy growth of the target; flower/bud count ofthe target; disease or anomaly regions of the target; estimated vaporpressure deficit (VPD) of leaves of the target; estimated temperature ofleaves of the target; or flower/bud density of the target.
 16. Thesystem of claim 13, wherein the determine one or more recommendations isperformed by a machine learning component.
 17. A computer-readablemedium comprising instructions stored therein, wherein the instructions,when executed by a system comprising one or more processors, cause thesystem to: receive data from one or more autonomous vehicles, the datapertaining to a horticultural field or one or more targets locatedwithin the horticultural field; analyze the received data to determineone or more conditions of the one or more targets; based on theanalyzing, determine one or more recommendations for addressing the oneor more conditions; send the determined conditions and recommendationsto a user interface; transmit data to the one or more autonomousvehicles that are indicative of follow-on actions for the target; andreceive additional data, when available, based on the follow-on actionsfor further analysis.
 18. The computer-readable medium of claim 17,wherein the one or more recommendations include at least one of changinga light intensity or a light spectrum of lighting, changing an amount ofwater or a frequency of a watering operation, changing an amount ofnutrients or fertilizer, changing a ratio of nutrients to fertilizer,changing an airflow, changing a temperature, changing an airflowintensity, changing an airflow direction schedule, or changing anautomated spraying of pesticides.
 19. The computer-readable medium ofclaim 17, further comprising determining a progress metric of thetarget, the progress metric indicative of progress of the targetrelative to predetermined milestones; wherein the analyzing comprisesdetermining that the progress metric is not meeting the predeterminedmilestones; wherein the one or more recommendations comprise generatingone or more actions to improve the progress.
 20. The computer-readablemedium of claim 16, wherein the follow-on actions include actions forautomation of at least one plant grower action for the target.